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transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec, and Bill Lapenta
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Page 1: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

AIRS Profile Assimilation -Case Study results

Shih-Hung Chou, Brad ZavodskyGary Jedlovec, and Bill Lapenta

Page 2: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Motivation for Profile Assimilation at SPoRT

The SPoRT Center seeks to improve short-term weather forecasts by the use of satellite-based observation.

AIRS data complement traditional upper-air observations in data-sparse regions (both ocean and land)

In contrast to AIRS radiances, profiles provide an easier assimilation method allowing regional and local end users (e.g. HUN WFO) to run NWP systems

Hyperspectral nature of AIRS sounder allows for high-resolution data

Page 3: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

AIRS Specifications

Aboard Aqua polar orbiter Early afternoon equator crossing

2378 spectral channels

3.7 – 15.4 μm (650 – 2675 cm-1)

3 x 3 footprints (50 km spatial resolution)

AMSU allows for retrievals in both clear and cloudy scenes

Version 4.0 Error Estimates (Tobin et al. 2006) 0.6-1.0K over ocean (± 50o latitude)

0.9-1.3K global ocean and land (in 1 km layers)

< 15% RH (in 2 km layers)

Page 4: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

AIRS Data Quality Indicators

Quality indicators (QIs) in prototype v5:

each profile contains level-specific QI

level-by-level error estimates for each T and q profile

QIs allow for the maximum amount of quality data to be assimilated

optimal use of QIs should produce an analysis that provides better initial conditions for the WRF

0700 UTC 20 November 2005 AIRS swath

Page 5: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Lessons Learned from Previous SAC

4 January, 2004

Pacific storm stalled off shore; limited its impact on land

Difficult to evaluate AIRS impact due to insufficient RAOB stations and stage IV precip data for verification

Mixed results for AIRS impact on forecast

Page 6: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Case Study: November 20-22, 2005

relevant to SPoRT interests in SEUS region ample verification data available over the Eastern US synoptic setting opportunity to eventually test both over-ocean and over-land AIRS profiles comparable CONUS domain to other SPoRT WRF for easy transfer to

operational applications

Rapidly intensifying storm off the eastern seaboard under forecasted by GFS, NAM, and SPoRT operational WRF

Case Selection

Surface analysis 11/22/05 12 UTCSurface analysis 11/20/05 12 UTC Surface analysis 11/22/05 12 UTC

LL

L

Page 7: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Analysis and Forecast Model ConfigurationWRF Model Configuration 36km domain with 150x360 grid

37 vertical levels Initialized with NAM analysis, LBC

updated every 3 h

ADAS Analysis Configuration Same horizontal domain as WRF 43 vertical levels separated by 500 m AIRS profiles are assimilated as

RAOBs using QIs to determine highest quality data

use Tobin et al. (2006) for observation error and standard model errors for background

Assimilation / Forecast 7h forecast used as background for

ADAS

L

L

L

WRF Domain for November 2005 Case Study

AIRS valid at 0700 UTC

7h FCST

00 UTC

11/20/0500 UTC

11/22/05Validation at 00 UTC and 12 UTC

00 UTC11/21/05ADAS

Page 8: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Impact of AIRS Profiles on ADAS Analysis

AIRS data have an cooling impact over Atlantic, but a warming impact on land

700 hPa Temp Difference

700 hPa Dew Point Difference

AIRS data have a major drying off east seaboard

Page 9: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Impact of AIRS Profiles on ADAS Analysis

07Z BKGD07Z AIRS07Z ADAS

20 November 2005Wallops Island, VA

AIRS shows cooling in the lower and upper troposphere AIRS shows drying above 900 hPa

Page 10: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Impact of AIRS Profiles on Initial Conditions

07Z BKGD07Z AIRS07Z ADAS00Z RAOB12Z RAOB

AIRS shows mid-troposphere cooling AIRS correctly detects the moistening of 700-500 hPa layer

20 November 2005Wallops Island, VA

AIRS shows cooling in the lower and upper troposphere

AIRS shows drying above 500 hPa

AIRS shows drying above 900 hPa

AIRS can spatially and temporally fill the gap between conventional observations

Page 11: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Control is too warm and moist at all tropospheric levels

Temperature and Moisture Impact

AIRS cools T by as much as 0.5oC (improvement) in much of troposphere; increases q bias at mid-levels AIRS reduces RMS error in T and q at most levels

Page 12: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

6-h Cumulative Precipitation Impact

CNTL over-forecast over the low center and under forecast over TN/AL

AIRS improves forecast compared to NCEP Stage IV data in region of heaviest precipitation

Page 13: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

6-h Cumulative Precipitation Impact

Bias Score

a measure of precip coverage

Precipitation under-forecasted

CNTL better at middle threshold; AIRS better at high

Equital Threat Score

a measure of precip loaction

AIRS outperforms CNTL at most threshold; similar at smallest threshold

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.254 2.540 6.350 12.70 19.05

Minimum Precipitation Threshold (mm)

Equi

tabl

e Th

reat

Sco

re (E

TS)

0

0.2

0.4

0.6

0.8

1

1.2

Bia

s Sc

ore

CNTL

AIRS

Qualitative Precipitation Forecast

Page 14: Transitioning unique NASA data and research technologies to the NWS AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec,

transitioning unique NASA data and research technologies to the NWS

Summary

AIRS Level-2 profiles provide valuable data over regions otherwise devoid of upper-air observations; they also fill the gap in time between the conventional observations

Level-specific QIs for AIRS profiles allow for the assimilation of the largest volume of highest quality data

AIRS data improves forecasts of T, q, and 6 h precip

Future plans involving AIRS

Real-time forecasts to evaluate long-term impact Select new case studies for in-depth analysis


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