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Mesoscale Data Assimilation and Prediction with Commercial Aircraft (TAMDAR) Observations. Yubao Liu Collaborators: Scott Swerdlin, Mark Anderson 1 , Tom Warner Laurie Carson, Ming Ge, Wei Yu and Francois Vendenberghe (NCAR/Research Applications Lab, USA; 1 AirDat llc.). TAMDAR and GLFE

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slide1

Mesoscale Data Assimilation and Prediction with Commercial Aircraft (TAMDAR) Observations

Yubao Liu

Collaborators: Scott Swerdlin, Mark Anderson1, Tom Warner Laurie Carson, Ming Ge, Wei Yu and Francois Vendenberghe

(NCAR/Research Applications Lab, USA; 1AirDat llc.)

  • TAMDAR and GLFE
  • The NCAR/ATEC RTFDDA
  • Data-denial experiment results
  • Comparison with NAM and RUC
  • Summary

BACIMO 2005

yliu@ucar.edu

slide2

Temperature

Moisture (two RHs)

Winds

Pressure

GPS height

Icing

Turbulence

Others

TAMDAR

Tropospheric Airborne Meteorological DAta Reporting

slide3

Motivations for TAMDAR

 Upper-air data are sparse and limited

  • Radiosondes: only observe twice daily
  • Satellite winds: single layer, clustered
  • ACARS: mostly at upper-troposphere
  • Wind profilers: low-spatial density
  • Indirect satellite and radar obs: exploratory
  • Insufficient lower-level moisture observation

TAMDAR is to fill the upper data gaps with high-frequency and high-density lower level soundings and plane observations

slide4

GLFE – Great Lakes Field Experiments

15 Jan -15 Jul 2005

Mesaba SAAB 340:

63 equipped

~400 flights a day between 75 airports

~20k obs a day

the ncar atec rtfdda system
The NCAR/ATEC RTFDDA System
  • PSU/NCARMM5 / WRF based,
  • Multi-scale: meso-g meso-a (dx = 0.5 – 45 km),
  • Rapid-cycling: flexibly at intervals of 1 – 12 hours,
  • FDDA:4-D continuous data assimilation,
  • Forecast( 0 – 48 hours), and
  • Real-time, retrospective and relocatable.

Main objective:To produce best-possible real-time local-scale analyses and nowcasts/forecasts by effectively combining a full-physics mesoscale model with all available observations

related bacimo presentations
Related BACIMO presentations
  • First introduced on: BACIMO-2001
  • Enhancements and applications: BACIMO-2003
  • WRF-transition: 2.08 (Knieval)
  • Feature-based verification:P2.04 (Rife)
  • Athens Olympics: 5.06 (Hahmann)
  • Probabilistic forecasts: 5.08 (Hacker)
  • Application modeling: 5.09 (Sharman)
  • 4DWX-on-MOVE (GMOD): 5.10 (Betancourt)
  • Global Climate Analysis Tool: P5.10 (Vendenberghe)
4 d continuous data assimilation and forecast

Wind Prof

ACARS

GOES

All WMO/GTS

Radars

t

FDDA

Forecast

MESONETs

TAMDAR

Etc.

4-D Continuous Data Assimilation and Forecast

New 12 - 48 h forecast every 3 hrs, using all obs up to “now”

RTFDDA

Regional-scale model, based on PSU/NCAR MM5 /WRF

Cold start

rtdda advantages of continuous relaxation

t

Coldstart

FDDA

Forecasts

RTDDA: Advantages of Continuous Relaxation
  • Allows to use all synoptic and asynoptic observations. In particular, it allows to fully weight time-space irregularly distributedobservations, such as TAMDAR data, according to the observation time, location and quality;
  • Mitigates dynamics and cloud/precipitation “spin-up” problem that exists in all cold-start operational models.

Both properties are critical for mesoscale analyses and short-term (0-12 hour) forecasts.

slide10

DX=36km

D1

Two real-time RTFDDA systems

AIRDAT:

with TAMDAR

AIRNOT:

w/o TAMDAR

DX=12km

D2

D3

D3: 400x400 km2

rtfdda observations a snapshot
RTFDDA Observations: A Snapshot

Sndgs Prof

Sat Aircraft

AirDat

850 hPa

> 600 hPa

SFC

00Z June 24 2005

Sat Aircraft

Sat Aircraft

600 - 350 hPa

< 350 hPa

slide12

RTFDDA NO-TAMDAR

04Z, 20041111, 3h-fcst

RTFDDA with TAMDAR

04Z, 20041111, 3h-fcst

WSR-88D Reflectivity

0419Z, 20041111

Frontal rainband (2)

Nov. 11, 2004

Model 3-h forecasts

slide13

Weak snowbands (1)

00Z, Dec. 09, 2004

Radar reflectivity

Without TAMDAR

With TAMDAR

RTFDDA

1h forecasts

WSR-88D

slide14

Without TAMDAR

Snowbands

18Z, Feb. 02, 2005

Radar reflectivity

RTFDDA Analyses

WSR-88D

With TAMDAR

slide15

Daily Evolution of

Forecast errors

At 850 hPa

Between

Jan. 29, 2005

And

Feb. 20, 2005

T

DIR

Q

SPD

With TAMDAR

W/O TAMDAR

RH

slide16

Temperature

12h fcst

Domain 2

Jan. 28 – Feb. 6

2005

6h fcst

Analysis

Water Vapor Mixing Ratio

Vector Wind Difference

Key:

W/O TAMDAR

WITH TAMDAR

slide17

Rainbands:15Z, March 12, 2005,1-h accu. rain (mm)

RTFDDA

4h Forecast

Stage IV

RUC-13km

3h forecast

WSR-88D

slide18

Rainbands

21Z, March 12, 2005

3-h accu. rain (mm)

RTFDDA presents better

Distribution and structures

RTFDDA

10h Forecasts

NAM-218

9h forecast

Stage II

slide19

Comparison of 3 hr Rain, valid at 20050815, 00Z

7h fcst

StageIV

RTFDDA

6h fcst

6h fcst

NAM218

RUC13

summary
Summary
  • TAMDAR data are evaluated with the NCAR RTFDDA system through data-denial experiments for mesoscale analyses and forecasts.
  • A general positive impact of TAMDAR was found in the analyses and forecasts of upper-air variables and surface precipitation.
  • RTFDDA analyses and 1 – 12h forecasts of surface precipitation with TAMDAR data outperform NOAA RUC and NAM running at similar resolutions.
  • The benefit of TAMDAR appears to fluctuate significantly with weather situations.
  • On-going work:

 study TAMDAR impact on cloud-scale models;

 optimize TAMDAR data assimilation algorithms;

 and provide modeling guidance for the next- phase, broad-scale TAMDAR implementation.