satellite-derived atmospheric motion vectors (amvs): tropical cyclone data assimilation and nwp...

Satellite-Derived Atmospheric Motion Vectors (AMVs): Tropical Cyclone Data Assimilation and NWP Impact Studies

Howard Berger1, C. Velden1, R. Langland2, C. Reynolds2

Hui Lui3, Jeff Anderson3, and Sharan Majumdar4.

1-Cooperative Institute for Meteorological Satellite Studies, Univ.-Wisconsin

2-Naval Research Laboratory, Monterey, CA

3-NCAR Institute for Mathematics Applied to Geosciences

4-RSMAS/University of Miami

Outline

• Brief Review of Recent Tropical Cyclone Studies Examining the Impact of AMVs

• NRL-CIMSS Collaborative Efforts using NAVDAS/NOGAPS with AMV Datasets Processed during TPARC

• NOPP Collaborative Efforts with NCAR and RSMAS/UMiami: Mesoscale WRF-DART AMV Data Impact Experiments

Recent Tropical Cyclone Studies Examining the NWP Impact of AMVs

• Goerss and Velden, 1998 MWR (NOGAPS)

• Soden and Velden, 2001 MWR (GFDL)

• Kelly, 2004 ECMWF Report (ECMWF)

• Zapotocny et al., 2005 WAF (NCEP/AVN)

• Goerss, 2009 MWR (NOGAPS)

• Langland, Velden and Berger, 2009 MWR (NOGAPS)

• Berger, Langland, Velden, Reynolds, 2011 JAMC (NOGAPS)

GFDL – Direct Assimilation of AMVs (Soden and Velden, 2001)

Impact of AMVs in ECMWF (Kelly, 2004) 200 hPA Vector Wind in the Tropics

Impact of AMVs in NCEP/AVN (Zapotocny et al., 2005)

Impact of AMVs in NCEP/AVN (Zapotocny et al., 2005)

Impact of AMVs in NOGAPS (Goerss, 2009)

- Forecast Hr- # of Cases

Impact of AMVs in NOGAPS (Goerss, 2009)

Forecast Hr

Katrina Case Study – Impact of GOES Rapid-Scan AMVs on NOGAPS Track Forecasts (Langland et al., 2009)

Katrina Case Study – Impact of GOES Rapid-Scan AMVs on NOGAPS Track Forecasts (Langland et al., 2009)

Katrina Case Study – Impact of GOES Rapid-Scan AMVs on NOGAPS Track Forecasts (Langland et al., 2009)

NOGAPS 48hr forecast of Hurricane Katrina positions verifying at 12 UTC 29 August 2005. RS AMV forecast (red) and CNL forecast (blue). Observed track (green). All positionsindicated at 12-hr intervals.

SPECIAL AMV DATA ANALYSIS AND NWP IMPACT STUDIES DURING TPARC

Howard Berger1, C.S. Velden1, R. Langland2, and C. A. Reynolds2

1-Cooperative Institute for Meteorological Satellite Studies, Univ.-Wisconsin

2-Naval Research Laboratory, Monterey, CA

Presented by C. Velden at the recent WMO DAOS committee meeting, Montreal,

and paper being submitted to JAMC

T-PARC Thorpex - Pacific Asian Regional Campaign

International field campaign during August – October, 2008 with special observing periods to investigate the formation, structure, intensification and prediction of tropical cyclones in the western North Pacific.

AMV Processing for TPARC

• Generated at CIMSS (essentially the operational NESDIS algorithm) by objectively targeting and tracking clouds and WV structures in sequential JMA MTSAT multi-spectral geostationary satellite images

• AMV heights are assigned using multispectral and semi-transparency techniques

• Apply objective quality control and assign quality indicators (QI)

• 1) Hourly datasets generated from routinely available MTSAT imagery (30-min hemispheric images), for the entire duration of the experiment

• 2) Datasets generated from special MTSAT-2 rapid-scan 15-minute images over the western North Pacific for limited periods during selected TCs

Special AMV Datasets for TPARC

MTSAT AMVs produced hourly (by UW-CIMSS) during TPARCExample: Typhoon Sinlaku -- 11th Sep. 2008

Example of AMVs from MTSAT-2 Rapid Scan imagesLeft: AMV (IR-only) field produced from routinely available 30-min sequence of MTSAT-1 images during Typhoon Sinlaku

Bottom Left: Same as above, but using a 15-min rapid scan sequence from MTSAT-2(better AMV coverage and coherence)

Bottom Right: Same as above, but using a 4-min rapid scan sequence (improved coverage/detail of typhoon flow fields)

NAVDAS-AR – NRL Atmospheric Variational Data Assimilation System-Accelerated Representer

NRL/FNMOC Analysis System(Naval Research Lab/Fleet Numeric Meteorology and Oceanography Center)

– Full 4D-VAR algorithm solved in observation space using representer approach

– Weak constraint formulation allows inclusion of model error– T239L42, model top at 0.04 hPa– More effective use of asynoptic and single-level data– More computationally efficient than NAVDAS for large # of obs– Adjoint developed for observation impact with real-time web

monitoring capability

NRL/FNMOC Analysis System(Naval Research Lab/Fleet Numeric Meteorology and Oceanography Center)

Superobbing strategy for AMVs:First remove any duplicates and obs from deselected levels, channels

Superob only like obs in a 2°lat/lon prism in a 50 mb layer

• Obs from same satellite, same channel, same time (or nearly so)

• At least two consistent observations required

• Require all winds to agree within specified criteria

Speed, u and v criteria vary as a function of windspeed

from 7 m/s for mean speeds less than 25 m/s

to 14 m/s for mean speeds greater than 75 m/s

u and v criterion = sqrt(((speed criterion)**2)/2) to ensure consistency with speed

criterion

Alternate direction criterion specified to be <20°

Innovations (superob – background) are calculated and used in NAVDAS to produce the analysis. Observation errors assigned to the superobs are assumed to be the same as for operational geo AMVs.

AMV Data Assimilation Experiments Collaboration with Rolf Langland and Carolyn Reynolds at the US Naval Research

Lab (NRL) in Monterey

• Continuously assimilate all hourly MTSAT AMV datasets using NRL 4DVAR during the 2-month TPARC period

• Assess impact on NRL/FNMOC NOGAPS TC forecasts:• CTL – All conventional and available special TPARC

observations (except for dropsondes), including hourly AMV datasets from MTSAT-1 (but no rapid-scan AMVs)

• EX1 (No-CIMSS AMV) – CTL with hourly AMVs removed• Rapid-Scan – CTL with Rapid-Scan AMVs included

NOGAPS track forecasts (nm) for TPARC

NOGAPS run with hourly and Rapid-Scan AMVs reduces TC track forecast errors notably at longer forecast times

AMVs reduce the larger track forecast busts at 120-hours

Mean Forecast Error

Example: Typhoon Sinlaku 120-h forecast

on Sept. 11, 2008 12UTC

MSLP (hPa)

Control w/ AMVs

NO-AMVs

Best-Track

Influence of transientmid-latitude troughs??

Example: Typhoon Sinlaku 120-h forecast

on Sept. 11, 2008 12UTC

MSLP (hPa)

Control w/ AMVs

Rapid-Scan

Best-Track

Influence of transientmid-latitude troughs??

500 hPa analyses in the Mid-Lats during TC Sinlaku

Hourly MTSAT AMVs have positive impact, particularly during the period of large NOGAPS track

forecast errors (NOAMV exp.) for Sinlaku

Summary

• Hourly satellite-derived AMVs allow for more consistent temporal coverage of the evolving atmospheric flow. The NRL 4DVAR DA can effectively utilize this frequently available information, resulting in improved NOGAPS TC track forecasts (e.g. TY Sinlaku), particularly at longer ranges (3-5 days).

• Rapid-Scan AMVs can better capture mesoscale flow features such as present in rapidly evolving TCs, leading to more precise kinematic diagnostics. They also show positive impact in NOGAPS TC track forecasts, and have promising applications in mesoscale data assimilation.

NOPP Collaborative Efforts with NCAR and RSMAS/UMiami: Mesoscale WRF-DART AMV Data Impact Experiments (Hui Liu and Jeff Anderson)

• Initial Case Studies: Typhoon Sinlaku (western North Pacific during TPARC), and Hurricane IKE (Atlantic in 2008)– Experiments with 6- and 3-hourly assimilation/analyses.

– EnKF - 32 ensemble members are used in the assimilations.

– Assimilations started one week before TC genesis.

– 9km moving nest grid with feedback to 27km grid in the 6-hourly (or 3-hourly) forecast when a TC is present.

– Assimilation and analyses on 27km grid only.

Analysis Experiments - Hurricane Ike

• Control (CTL): 6-hourly analysis cycle. All routine operational data (Radiosonde, AMVs, surface, aircraft) and NHC/JTWC advisory TC positions.

• CIMSS-RS6h: CIMSS rapid scan AMVs replace operational AMVs, 6-hourly analyses.

• CIMSS-RS3h: As above, but 3-hourly analyses (3-hour cycle may be needed to increase benefits of the rapid scan obs).

__________________________________________________

• Only the AMVs at the analysis times are used (no off-time assimilation attempts yet).

• Only analyses are finished at this point (no forecast results yet).

Example of Operational AMVs for Ike

Example of CIMSS Rapid-Scan AMVs for Ike

• Radiosondes,

Wind Analysis Increment at 300 hPa (12Z Sep 02, 2008)

CIMSS-RS6hCIMSS-RS6h

CTL CTL

Track and Intensity Analyses for Hurricane Ike

Summary

• Recent studies regarding the impact of satellite-derived AMV observations on NWP tropical cyclone forecasts show positive results. AMVs are still very much relevant in the tropics!

• Efforts to optimize the assimilation of AMVs continue on two fronts:

1) Global assimilation/models (thinning, superobbing, better utilization of AMV quality indicators, hourly assimilation).

2) Mesoscale assimilation/models (use of rapid-scan AMVs, EnKF methods to optimally assimilate high density space/time AMV obs, use of AMV data in lieu of or to augment TC bogus vortex pseudo-obs, focus on TC intensity/structure improvements).

Extra Slides

NOGAPS track forecasts (nm) for TPARC

• NOGAPS run with hourly and Rapid-Scan AMVs reduces TC track forecast errors notably at longer forecast times

Forecast Time (hrs)

0 12 24 36 48 60 72 84 96 108 120

Controlw/ AMVs

22 39 70 93 114 151 213 195 167 248 317

No-AMV 22 40 67 91 108 154 227 248 245 365 450

Rapid-Scan

25 45 78 111 122 158 210 174 135 215 260

#CASES 22 20 18 16 14 13 12 11 9 8 7

Impact of AMVs in ECMWF (Kelly, 2004)

NH

SH

TP