Validation of Atmospheric Infrared Sounder (AIRS) Data Using GPS Dropsondes in Tropical

Cyclone Environments

Eddy HildebrandUniversity of Miami-RSMAS

Miami, FL8 June 2010

Objectives

• Validate the performance of AIRS in measuring temperature and moisture profiles in the environments surrounding Tropical Storms Irene (2005) and Debby (2006) and Hurricane Helene (2006)

• Temporally average AIRS data to show interseasonal and intraseasonal variability in tropical North Atlantic moisture

• Compare AIRS moisture data with other satellite measurements such as SSM/I

• Examine the relationship between dry air and intensity change of tropical cyclones– Dry air from Saharan Air Layer (SAL) and/or mid-latitude origins– Also consider SHIPS wind shear and SST

Defining the SAL

• Occasional dust outbreaks (every few days) originating over Africa

• Most frequent in June/July (Dunion 2010, Dunion and Marron 2008)

• Propagate across the Atlantic at nearly the same speed as tropical waves

Figures: Dunion 2010

Figure: Dunion and Velden (2004)

- Black line: Jordan (1958) Mean Tropical Sounding

- Solid blue line: mean moist tropical/non-SAL sounding

- Dashed blue line: mean SAL sounding

From GPS dropsondes launched in environments surrounding four tropical cyclones

Tracking the SAL

VIS, IR, and MW satellites (GOES, Meteosat, SSM/I, AIRS)

Radiosondes and GPS dropsondes – sparse coverage

(7.3 micron IR channel; sensitive to low & mid level moisture)

Figures: CIMSS

Tracking the SAL

• Air Resources Laboratory (ARL) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model

– Uses NCEP/NCAR reanalysis

– Backward trajectories indicating air parcel origins

• Navy Aerosol Analysis and Prediction Scheme (NAAPS)

Atmospheric Infrared Sounder - AIRS• Aboard Aqua satellite (part of A-Train) launched in May 2002• 2378 spectral channels• Expected performance ability (Tobin et al. 2006):

– Temperature – 1K RMS differences in 1km layers– RH – 20% in 2km layers

• Vertical profiles ~50km horizontal resolution with 28 vertical levels (Wu et al. 2006)

Courtesy: www.nasa.gov

AIRS Dust Flag

• Unitless integer indicating presence of dust

• Comparison of radiance values

• Only valid in clear atmosphere over ocean

• Clouds above the dust lead to a failed algorithm

AIRS Dust Flag

AIRS/GPS Dropsonde Comparison

• “Match” individual AIRS profiles with NOAA/HRD GPS dropsondes from Tropical Storms Irene (2005) and Debby (2006) and Hurricane Helene (2006):

– Spatial distance (< 50 km)– Time difference (< 3 hr)– Error flag (< 3584)

• Why a strict spatial distance?– Horizontal moisture gradients

along the SAL edge are high

Figure courtesy: CIRA

• AIRS/GPS dropsonde temperatures compare well over most of the troposphere

• Biggest issue is with T inversion at SAL base – maybe due to AIRS vertical resolution?

Mixing Ratio Histograms & PDF

• TS Irene (7 August 2005)

• Bimodal peak in MR at 850 hPa (SAL and moist tropical)

850 hPa

1000 hPa

• 35 GPS dropsondes released in SAL environments

• 84 matching AIRS profiles

• Greatest GPS dropsonde/AIRS mixing ratio difference: ~3.1 g/kg at 850 hPa

• 5 moist tropical GPS dropsondes

• 16 AIRS matching profiles

• Small sample size (SALEX missions, focus was on SAL regimes)

Sensitivity to Distance Criterion• Initially 50 km between AIRS profile and GPS dropsonde• What about 25 km? 75 km? Do AIRS statistics change?

Correlation Coefficients RMS Error

25 km 50 km 75 km 25 km 50 km 75 km

Low level (1000-850) 0.972 0.975 0.986 1.54 2.39 2.58

Mid-level (850-400) 0.972 0.968 0.978 1.31 1.65 1.59

Deep layer (1000-300) 0.985 0.987 0.986 1.13 1.57 1.57

25 km

75 km50 km

Total Precipitable Water

• Amount of water vapor contained in a column of air extending from the surface to the top of the atmosphere

• Vertical integration of MR

• ~99.5% of atmospheric moisture is below 250 hPa (~92% below 500 hPa) (Dunion 2010)

AIRS Mixing Ratio Cross-sectionsAIRS struggles with moisture retrievals near TCs (and in high terrain) so why use it?

- Vertical distribution of moisture

Hurricane Helene (15 Sep 2006)

AIRS Mixing Ratio & TPW Climatology

All AIRS MR values from 0-40N 15-90W

June

August

October

June-October

What if we remove the mean?

June 16-30

August

Figure: Dunion (2010)

-Aug 2005 has more moisture in central & western Atlantic than Aug 2006

Aug 2005 Aug 2006

Aug 05 Aug 06

Sep 05 Sep 06

Sep 2005 Sep 2006

-Sep 2005 has more moisture in Caribbean and less in NE Atlantic than Sep 2006

Consider Aug 05 & Sep 06 and the difference field between the two months

Aug 05 drier in NE Atlantic – stronger subtropical high?

Aug 2005

Sep 2006

Jun Jul

Aug Sep

Oct

Jun Jul

Aug Sep

Oct

Summer 2005 Summer 2006

-Summer 2005: more moisture in western Atlantic basin

-Summer 2006: higher standard deviation over west Africa

Dry Air and Intensity Change• Named storms within the domain during AIRS lifetime (’03-’08): 57• Storm days: 327 (176 intensification, 67 steady, 84 weakening)

– Exclude days when storms were centered over land • Determine intensity change based on quadrants with dry air

– Dry air defined as TPW < 45 mm (Dunion 2010)– Radius of 400 km from storm center (Shu and Wu 2009)

• Intensity change based on dry air and shear/SST

Domain: 5-30N 20-80W

Filtered AIRS Data

Remove pixels surrounding cloud-flagged pixels if TPW < 25 mm

NENENW NW

SWSW SESE

Filtered AIRS data:

-Peaks slightly broader in weakening cases (ex: SE quad)

-Peak ~50 mm in all quadrants regardless of intensity change

-Sharp tail toward higher values; gradual tail toward lower values

Microwave Data

Microwave data:

-Sharper peaks for intensifying cases

-Peaks slightly higher for intensifying cases (~55 mm vs ~50 mm for weakening)

-Larger tail toward lower TPW values for weakening cases

-Tails much smaller than AIRS data

NW NW NENE

SW SW SE SE

AIRS - Intensifying

AIRS - Weakening

MW - Intensifying

MW - Weakening

AIRS and microwave TPW histograms:

-Peaks sharper for microwave data

-Sharper tail toward lower TPW values in microwave data

Dry Air and Wind Shear

Low wind shear (< 15 kt or ~8 m/s):

-Intensification more likely no matter how much dry air is present

-Likelihood of intensification decreases with increasing dry air

High wind shear (> 15 kt or ~8 m/s):

-Weakening more likely

-Likelihood of weakening increases further when there is more dry air on the east side of the storm

I IW W

Dry Air and SST

High SST (> 28C):

-Intensification generally more likely

-Likelihood of of weakening increases with dry air

Low SST (< 28C):

-Similarities to high SST plot

-Storms can still intensify with SST < 28C

-Weakening becomes more likely here too with more dry air

I

I

W

W

Conclusions and Future Work AIRS can differentiate between a dry (SAL or mid-latitude air) environment and

a moist tropical one

Deep convection, especially that associated with tropical cyclones, impacts AIRS moisture retrievals

Comparison of AIRS and GPS dropsonde data suggests AIRS struggles with low-mid level moisture (~3 g/kg dry bias in SAL cases at 850 hPa)

AIRS total precipitable water can be used on individual days and for temporal averaging to determine variability in tropical North Atlantic moisture

AIRS struggles in areas immediately surrounding cloud-flagged pixels – extremely low TPW near the core of tropical cyclones

Relationship between dry air and intensity change suggests that dry air combines with other factors (high shear and/or low SST) to increase the chances of weakening

Expand to other ocean basins – no SAL, but they all have mid-latitude dry air

Acknowledgements

• Committee:– Chidong Zhang (chair)– Sharanya Majumdar– Jason Dunion (NOAA/AOML/HRD)

• Friends at RSMAS, especially those in MPO, and especially those in MPO who woke up “early” to get here by 11

• Wetlab and award-winning $2 pints

Award

References• Dunion, J. P., 2010: Re-Writing the climatology of the tropical North Atlantic and Caribbean Sea

atmosphere. J. Climate (Accepted).• Dunion, J. P., and C. S. Marron, 2008: A reexamination of the Jordan mean tropical sounding

based on awareness of the Saharan Air Layer: results from 2002. J. Climate, 21, 5242–5253.• Shu, S., and L. Wu, 2009: Analysis of the influence of Saharan air layer on tropical cyclone

intensity using AIRS/Aqua data. Geophys. Res. Lett., 36, L09809, doi:10.1029/2009GL037634.• Tobin, D. C., and Coauthors, 2006: Atmospheric Radiation Measurement site atmospheric state

best estimates for Atmospheric Infrared Sounder temperature and water vapor retrieval validation. J. Geophys. Res., 111, D09S14, doi:10.1029/2005JD006103.

• Wu, L., S. A. Braun, J. J. Qu, and X. Hao, 2006: Simulating the formation of Hurricane Isabel (2003) with AIRS data. Geophys. Res. Lett., 33, L04804, doi:10.1029/2005GL024665.

Courtesy: Chidong Zhang