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Global Impact of Satellite Data on NWP. Model is GFS. Satellite data include HIRS, AMSU, Quickscat, and all geo winds. Courtesy of Tom Zapotocny. Current Users :

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slide1

Global Impact of Satellite Data on NWP

Model is GFS. Satellite data include HIRS, AMSU, Quickscat, and all geo winds.

Courtesy of Tom Zapotocny

slide2

Current Users:

  • European Centre for Medium-Range Weather Forecasts (ECMWF; Lueder von Bremen and Jean-Noel Thepaut) - Using winds in operational system.
  • NASA Global Modeling and Assimilation Office (GMAO; Lars Peter Riishojgaard and Yanqiu Zhu) - Using winds in operational system.
  • Japan Meteorological Agency (JMA; Masahiro Kazumori) – Using winds in operational system (Arctic only)
  • Canadian Meteorological Centre (CMC; Real Sarrazin) – Using winds in operational system.
  • US Navy, Fleet Numerical Meteorology and Oceanography Center (FNMOC; Pat Pauley and Chuck Skupniewicz) – Using winds in operational system.
  • UK Met Office (Mary Forsythe and Howard Berger) – Using winds in operational system.
  • National Centers for Environmental Prediction NCEP/EMC; John LeMarshall, Jim Jung, Tom Zapotocny) - Engaged in impact studies. Plan to go operational in June 2005.
  • Deutscher Wetterdienst (DWD; Alexander Cress) – Using winds in experimental system
  • NCAR Antarctic Mesoscale Model (AMPS; Dale Barker) – Beginning to test the winds over Antarctica.
  • Other (incomplete information): Chinese meteorological agency, one or more countries involved in HIRLAM.
  • Potential Users:
  • Forecast offices: NWS Alaska (Gary Hufford); McMurdo – Not using them yet, but will.
  • ECMWF Reanalysis (Fedor Mesinger) – Would also use historical AVHRR winds if available.

MODIS Winds in NWP

nasa global modeling and assimilation office gmao lars peter riishojgaard and yanqiu zhu
NASA Global Modeling and Assimilation Office (GMAO)(Lars Peter Riishojgaard and Yanqiu Zhu)
  • MOWSAP (MODIS Winds Special Acquisition Period): November 8, 2003-January 31, 2004
    • Winds generated by CIMSS and by NESDIS (pre-operational mode)
      • Different code bases
      • Different backgrounds for QC and height assignment
    • Winds generated from MODIS Aqua and MODIS Terra imagery
    • Assimilation experiments by GMAO, ECMWF, Met Office, CMC
    • Standard common diagnostics
    • GMAO experiments: 5-day forecast every other day; 42 members in the ensemble
forecast skills verified against their own analyses
Forecast skills verified against their own analyses

(CIMSS winds)

(NESDIS winds)

Forecast scores are the correlation between the forecast geopotential height anomalies, with and without the MODIS winds, and their own analyses.

slide8

Forecast Busts (GMAO)

Southern Hemisphere Extratropics

Arctic

Blue is forecast with MODIS winds; red is control run

summary of gmao results
Summary of GMAO Results
  • MODIS winds complement other observations in the high latitudes; more so in the SH than in the NH due to the current data sparsity
  • Consistency of data delivery is acceptable; timeliness remains problematic; being addressed using DB capability
  • Based on independent verification and innovation statistics, the quality of the information is acceptable
  • Positive overall contribution to forecast skill; substantial in the SH
slide11

European Centre for Medium-Range

Weather Forecasts (ECMWF)

(Lueder von Bremen and Jean-Noel Thepaut)

Assimilation experiments

- 3DVAR, T159/159: 5 March—3 April 2001

- 4DVAR, T511/159, compared to ops: 13 July—16 August 2002…

- 4DVAR, T511/159: 5—15 March 2002…

Use same assimilation settings as for geostationary AMVs, except for blacklisting of lower level winds:

Winds used:

IR, WV cloudy and clear

Over land: IR and WV winds above 400 hPa.

Over sea: IR winds above 700 hPa, WV above 550 hPa.

slide13

ECMWF Mean and Difference Wind Fields:

400 hPa

Arctic: MODIS winds act to strengthen the circulation at upper levels; at lower levels the difference field suggests a weakening of the local circulation.

Antarctic: MODIS winds act to strengthen the flow around the Antarctic Peninsula.

slide14

Forecast Impact (3DVAR)

Anomaly correlation for 500 hPa geopotential, each experiment verified against its own analysis. Forecast scores are the correlation between the forecast geopotential height anomalies, with and without the MODIS winds, and their own analyses.

Northern Hemisphere

Arctic (N of 65N)

slide15

Forecast Impact (3DVAR, cnt’d)

Anomaly correlation for 500 hPa geopotential, each experiment verified against its own analysis.

Southern Hemisphere

Antarctic (S of 65S)

slide16

Forecast Impact (4DVAR)

Anomaly correlation for geopotential, each experiment verified against operational analysis.

NH, 1000 hPa

NH, 500 hPa

slide17

Forecast Impact (4DVAR, cnt’d)

Anomaly correlation for geopotential, each experiment verified against operational analysis.

SH, 1000 hPa

SH, 500 hPa

slide18

Forecast Impact (4DVAR, cnt’d)

N.POLE

N.ATL

EUROPE

E.ASIA

slide19

ECMWF: Error Propagation to the Midlatitudes

This animation illustrates the propagation of analysis errors from the poles to the midlatitudes for one case study. Each frame shows the 500 hPa geopotential height for forecasts from 1 to 5 days in 1 day increments. The solid blue line is the geopotential from the experiment that included MODIS winds; the dashed black line is the control (CTL) experiment without MODIS winds. Solid red lines show positive differences in the geopotential height (MODIS minus CTL), and thick dashed green lines show negative differences.

The area of large positive differences near the Beaufort Sea (north of Alaska) moves southward over the 5-day period. The CTL run is forming a deeper trough over central Alaska and then over the Pacific south of Alaska than the MODIS run.

The 5-day MODIS forecast verifies better against the subsequent analysis (not shown), so the initial analysis for this MODIS forecast was closer to the “truth” than the CTL (positive impact on forecast). The propagation of differences is therefore also a propagation of analysis errors in the CTL forecast.

Better observations over the poles should improve forecasts in the midlatitudes.

slide20

Error Propagation to the Midlatitudes: Snowfall

Accumulated snowfall forecasts (mm water equivalent) over Alaska for 20 March 2001. Inclusion of MODIS winds in the analysis can produce a more accurate forecast. At right is the snowfall from the 5-day Control forecast (no MODIS winds); below left is the snowfall from the 5-day forecast that included the MODIS winds in the analysis; below right is the snowfall from a 12-hr forecast for verification (“truth”).

slide21

Canadian Climate Centre (CMC)

(Real Sarrazin)

  • Now: - Uniform resolution of 100 km (400 X 200 X 28)
      • - 3D-Var at T108 on model levels, 6-hr cycle, use of raw
      • radiances from AMSUA, AMSUB and GOES
  • 2004: - Resolution to ~35 km (800 X 600 X 56) - 4D-Var assimilation, 6-hr time window with 3 outer loops at full
      • model resolution and inner loops at T108
      • - new datasets: profilers, MODIS winds, AMSUA on AQUA
      • - EnKF for EPS
  • 2005: - Top at 0.1 hPa (instead of 10 hPa) with additional AMSUA and
      • AMSUB channels (Clément`s presentation)
      • - assimilation of AIRS, MSG, MTSAT, QuikScat, SSM/IS
      • - revised 4D-Var statistics
  • 2006-07: - Additional datasets: IASI, GIFTS, COSMIC…
  • - very large increase in volume of assimilated data
slide22

CMC 3DVAR Impacts

Nov 5 – Dec 15, 2003

Slight positive impact in the Arctic, positive in the Antarctic and SH, neutral in NH

slide23

MODIS winds: latest results at the Met OfficeMary Forsythe (1), Howard Berger (1,2)(1) Met Office, Exeter, UK(2)UW-CIMSS Madison, WI

distribution of modis winds

100

200

300

400

500

Pressure (hPa)

600

700

800

900

1000

0

20

40

60

80

100

120

Number of winds

Thousands

Distribution of MODIS winds

NESDIS Feb 04

Terra NH

Terra SH

Aqua NH

Aqua SH

100

200

300

400

500

Pressure (hPa)

600

700

CSWV

WV

CSWV

IR

800

900

1000

0

20

40

60

80

100

120

Number of winds

Thousands

  • More Terra winds in NH than SH
  • More Aqua winds in SH than NH
  • SH winds (summer) peak at lower pressures
some statistical comparisons to meto background
Some Statistical Comparisons to MetO background

NESDIS Feb 04

Comparing Satellites Comparing Channels

IR

Terra NH

slide28

Verification vs OBS and ANAL

3

vs OBSERVATIONS

vs ANALYSES

2

1

% RMS difference (experiment – control)

0

-1

-2

NH H500 T+72

NH H500 T+24

NH H500 T+48

SH H500 T+72

NH PMSL T+96

SH PMSL T+24

SH PMSL T+48

SH PMSL T+72

NH PMSL T+24

NH PMSL T+48

SH H500 T+24

SH H500 T+48

SH PMSL T+96

NH W250 T+24

SH W250 T+24

NH PMSL T+72

NH PMSL T+120

SH PMSL T+120

Trop W850 T+24

Trop W850 T+48

Trop W850 T+72

Trop W250 T+24

slide29

Conclusions

  • Winds from Aqua and Terra are of comparable quality.
  • Some benefits were observed in the forecasts when CSWV winds were included, blacklisting was modified and the thinning box was increased.
  • The NESDIS MODIS product compares better with MetO background than the CIMSS product.
  • The latest MODIS experiment at the Met Office, using the NESDIS MODIS winds, is the first to give neutral to positive impact in all latitude bands.
  • Direct broadcast should allow more MODIS winds to be used in the Met Office assimilation system.
modis polar winds assimilation experiments at jma

MODIS Polar Winds Assimilation Experiments at JMA

Masahiro Kazumori, Yoshiyuki Nakamura

Numerical Prediction Division

Japan Meteorological Agency

impacts on forecast
Impacts on Forecast
  • Anomaly Correlation and RMSE at 500hPa
    • Large improvement for both seasons in the N.H.
    • Neutral for the Tropics and the S.H.

(no MODIS assimilation in these regions)

    • Change in the Arctic spread to the lower latitudes.
  • RMS forecast error of wind vectors were reduced.
    • Especially, improvements at 500hPa was remarkable.

( 500hPa was the level with maximum data number)

  • Improvements on typhoon track prediction
    • Small, but positive impacts were found at the later stage in the forecasts.
z500 anomaly correlation
Z500AnomalyCorrelation

Red:TEST

Blue:CNTL

Large improvements were found for the Northern Hemisphere.

rms forecast error difference for 500hpa z test minus cntl
RMS forecast error difference for 500hPa Z TEST minus CNTL

(m)

(m)

(m)

  • Monthly mean difference for July 2003

1day forecast

3day forecast

5day forecast

Positive Impacts of MODIS (negative difference) spread to mid-latitudes with procession of forecast.

impacts on forecast1
Impacts on Forecast
  • Anomaly Correlation and RMSE at 500hPa
    • Large improvement for both seasons by 9-day forecasts
    • Neutral for the tropics and the S.H.

(no MODIS assimilation)

    • Change in the Arctic spread to the lower latitudes.
  • RMS forecast error of wind vectors were reduced.
    • Especially, improvements at 500hPa were remarkable.

( 500hPa was the level with maximum data number)

  • Improvements on typhoon track prediction
    • Small, but positive impacts were found at the later stage in the forecasts.
zonal mean of rmse of wind speed at 500hpa 5 day forecasts january2004
Zonal mean of RMSE of Wind Speed at 500hPa(5-day forecasts,January2004)

(m/s)

Improvement

(m/s)

Improvement

impacts on forecast2
Impacts on Forecast
  • Anomaly Correlation and RMSE at 500hPa
    • Large improvement for both seasons by 9-day forecasts
    • Neutral for the tropics and the S.H.

(no MODIS assimilation)

    • Change in the Arctic spread to the lower latitudes.
  • RMS forecast error of wind vectors were reduced.
    • Especially, improvements at 500hPa was remarkable.

( 500hPa was the level with maximum data number)

  • Improvements on typhoon track prediction
    • Small, but positive impacts were found at the later stage in the forecasts.
mean positional error of typhoon track predictions
Mean positional error of typhoon track predictions
  • 22 events in July 2003
  • Neutral or slightly positive at the later stage in the forecast time.
summary
Summary
  • The MODIS polar winds assimilation experiments were performed at JMA
    • Period:July 2003, January 2004
    • Used data:Aqua/MODIS,Terra/MODIS in the Arctic
    • QC
      • Over land, above 400hPa for IR and WV
      • Over ocean, above 700hPa for IR and 550hPa for WV
    • Data thinning 150km
  • Results
    • Improvements on the analysis and first guess in the Arctic.
    • Large positive impacts on forecasts for both seasons. ( height, temperature, wind fields)
    • Improvements on the typhoon track prediction.

JMA started the assimilation of MODIS polar winds in the Arctic operationally at 27 May 2004.

slide39

Usage of MODIS winds

Usage: over Land and Oceans

IR above 1000 hPa; over Antartica only above 700 hPa

Wvcloudy above 700 hPa

FG check: asymmetric to remove negative OBS-FG bias

Thinning: 1 wind per pre-defined thinning box (oper.: 70 x 70 km; 15 vertical layers).

Same observation errors as SATOB

Experiments:

CTL : Routine assimilation and forecast system using different kinds of

observing systems (synops, buoys, aircraft, radiosondes, satob, satem, paob)

Exp : CTL plus MODIS winds for a summer and an autuum case

7th Int. Winds Workshop

Alexander Cress

slide41

Anomalie correlation

date: 2003061200 - 2003070900

28 forecasts; verified against routine analyses

SH

forecast time h

forecast time h

7th Int. Winds Workshop

Alexander Cress

slide42

Anomalie correlation

date: 2003061212 - 2003070912

28 forecasts; verified against routine analyses

forecast time h

forecast time h

7th Int. Winds Workshop

Alexander Cress

slide43

500 hPa Geopotential

Northern Hemisphere

VV= +60h

7th Int. Winds Workshop

Alexander Cress

slide44

Summary of MODIS wind results

  • Good correspondence between MODIS wind statistics and similar statistics

for AMV winds from geostationary satellites

  • FG statistics look very similar between Terra and Aqua.
  • Some differences exists in the OBS-FG, OBS-ANA statistics between

Terra and Aqua over Antartica

  • Large impact on polar analysis by introducing analysis increments in data

void areas

  • Impact of MODIS winds depends strongly on season and occasions

in which the interaction between polar and midlatitude flow patterns

is particularly intense

7th Int. Winds Workshop

Alexander Cress

modis wind assimilation ncep s global forecast system

MODIS Wind Assimilation NCEP’s Global Forecast System

John Le Marshall (JCSDA)

James Jung (CIMSS)

John Derber (NCEP)

Jaime Daniels (NESDIS)

International Winds Workshop

June 14, 2004

slide46

Global Forecast System Background

  • Operational SSI (3DVAR) version used
  • Operational GFS T254L64 with reductions in resolution at 84 (T170L42) and 180 (T126L28) hours. 2.5hr cut off
slide47

The Trial

  • Used MOWSAP (MODIS Winds Special Acquisition Period) data
    • NESDIS (Ops.) generated AMVs
    • 1 Jan – 15 Feb ‘04
    • Terra & Aqua satellites
  • Similar assimilation methodology that used for GOES AMVs
  • Middle image used for tracers
  • AMVs largely used NESDIS QC
  • Post NESDIS QC used, particularly for gross errors cf. background and for winds above tropopause
  • Winds assimilated only in second last analysis (later “final” analysis) to simulate realistic data availability.
discussion and conclusions
Discussion and Conclusions
  • Overall positive impact
  • AMV observation fit to background worst over land and ice near the surface. AMVs still have positive impact in lower levels
  • Post NESDIS QC used, particularly for gross errors cf. background and for winds above tropopause
  • Repeat with emissivity corrections for ice and snow in GFC
  • Planned for implementation in next upgrade
slide53

RMS (m/s)

Forecast (days)

Impact of MODIS Winds on Hurricane Track Forecasts

slide54

AVERAGE HURRICANE TRACK ERRORS (NM)

FREQUENCY OF SUPERIOR HURRICANE PERFORMANCE (%)*

  • Percent of cases where the specified run had a more accurate hurricane position than the other run.
  • Note: These cases are for hurricanes in the subtropics.
slide55

Fleet Numerical Meteorology and Oceanography Center (FNMOC)

(Pat Pauley, Randal Pauley)

  • NAVDAS
  • NRL Atmospheric Variational Data Assimilation System
  • Three-dimensional variational data assimilation system cast in observation space
    • Determines an optimal state of the atmosphere using the error statistics of the observing and forecast systems
    • Directly assimilates observations without conversion to the model state variables
    • Operates on both global and regional model domains
  • Transitioned to operational use at FNMOC on 1 Oct 2003
  • Original implementation included geostationary feature-track winds but not MODIS winds
slide56

Use of feature-track winds in NAVDAS

  • Winds superobbed in 2 ° “prisms”
  • “Prisms” are nearly square regions
    • 2 ° latitude by 2 ° longitude at equator
    • Width adjusted to keep area nearly constant
    • Integer number of prisms in each latitude band
  • Superobbing subject to several constraints
    • At least two consistent observations
    • One or two outliers may be rejected
    • Wind speeds within 7 m/s
    • u, v components within 5 m/s or directions within 20 °
    • Innovations (ob – background) averaged and used in NAVDAS at the average location
  • Observation error same as for geostationary feature-track winds
slide57

500 mb height anomaly correlation for 00Z forecasts. “NOGAPS” is the control run; “BETA” includes MODIS winds.

Increased skill at ranges beyond 48 hours in the Arctic (left) and Northern Hemisphere (right)

slide58

500 mb height anomaly correlation for 00Z forecasts. “NOGAPS” is the control run; “BETA” includes MODIS winds.

slide59

FNMOC Results and plans

  • Early results show MODIS winds have a positive impact in the Northern Hemisphere, but a negative impact in the Southern Hemisphere.
  • More recent testing looks better in the Southern Hemisphere but less positive in the Northern Hemisphere.
  • Began use of MODIS winds in operational forecast system in October 2004.
  • Future items to test include a more stringent QC (e.g., omitting lower quality winds, lower-level Southern Hemisphere winds) and increasing the observation error.
slide60

Recommendation from WMO NWP Workshop held in 9-12 March 2004 in Alpbach, Austria (attended by 52 NWP experts representing more than 20 NWP centers)

“polar wind observations should be developed further and an operational follow-on to the MODIS winds be secured (this will require a water vapour channel on the operational imagers on NPOESS and METOP). Timeliness of data delivery can be addressed through direct data read-out. The number of stations with direct read-out capability should be increased. Such data should be made available directly to the processing centres.”

slide61

3D display of impact of polar winds on GFS

84 hr forecast of 400 hPa heights

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