# Presentation: 499938

## Developing an AIRS-based Precipitation Algorithm for High-Latitude Application in Preparation for the GPM Era

- E.J. Nelkin1,2, G..J. Huffman1,2, R.F. Adler1,3, D.T. Bolvin1,2,
- J. Susskind1, J.M. Haynes4
- 	1:  NASA/GSFC Laboratory for Atmospheres
- 	2:  Science Systems and Applications, Inc.
- 	3:	ESSIC, University of Maryland, College Park
- 	4:	Colorado State University

**Outline**
- 1.	Background
- 2.	The TOVS/AIRS algorithm
- Calibration with AMSR-E
- 4.	Comparisons with CloudSat
- Summary
- Future Work

## 1.	BACKGROUND

- Retrievals are more challenging at high latitudes
- -	Different T, RH profiles; sfc. T; tropopause and melting levels
- -	Generally light precipitation
- -	Frozen/icy surface knocks out scattering channels
- Validation is
- also more
- challenging
- -	Gauges are
- 	sparse
- -	Gauge
- 	undercatch
- 	more severe
- -	Radar has
- 	difficulties
- 	with snow
- 	and bright
- 	band

- Example 3B40RT, part of the TMPA
- (TRMM Multi-satellite Precipitation Analysis )

## 1.	BACKGROUND –

**	****GPCP Monthly SG**
- Version 1 deficiencies
- -	Data voids at high lat.
- -	Low values in high-lat. 		ocean
- Susskind et al. (1997) TOVS adapted for use in Version 2
- -	Recalibrated to SSM/I at 	mid-lat., gauge at high 		lat.
- Since May 2005, GPCP V2
- has used an AIRS version of
- the Susskind algorithm.

## 2.	THE TOVS/AIRS ALGORITHM

- -	Susskind et al. (1997) developed a calibrated cloud volume proxy from TOVS
*	**Precip*  =  *revised cloud depth*  *  *cloud fraction*  *  ƒ ( latitude, season )
- -	The calibration is TOVS _swath_ data vs. _daily_ FGGE station precip data
- -	Results show low precip rates, very high fractional occurrence
- 	•  done as a regression
- 	•  uses instantaneous data as a proxy for daily data
- 	•  has only one sample for the day
**-****	****Goal: Take advantage of the strengths of AMSR-E and CloudSat to boost**
**	****	****AIRS rates, reduce occurrence, and apply results at high latitudes.**

*cloud top ht.*  –  ( *scaled RH*  +  *scaled cloud fraction* )

- 0 = sat. sfc500 mb
- 9 = dry        “

- 0    = overcast
- 4.5 = clear

## 3.	CALIBRATION WITH AMSR-E

**3.****	****CALIBRATION WITH AMSR-E**
- Both AIRS and AMSR-E are aboard the EOS Aqua satellite, yielding synchronized match-ups.
- Susskind et al. (1997) AIRS calibrated to AMSR-E for July 2006
- -	calibration by lat. bands:
- 	Ocean:	90-30°N, 30°N-S, 30-90°S
- 	Land:	90-40°N, 40·°N-S, (40-90°S)
- 	Coast:	global

- Cal.AIRS  (mm/d)  July 2006

- AMSR  (mm/d)  July 2006

- Diff.  (mm/d)  July 2006

- -	Note opposing within-band (east-west) 	differences
- -	Implies regime dependence – same 	scaled cloud volume maps to different 	AMSR-E rain rates in different places

## 3.	CALIBRATION WITH AMSR-E (cont.)

- Example of AIRS filling in a feature over snow where AMSR cannot reliably estimate

**AMSR-E**

**Calibrated**
**AIRS**

- Land precip feature

## High-latitude Southern Hemisphere

- winter case, with precipitation
- near the center of the swath

- 9-10 July 2006, 22:30-01:30 UTC

- Original AIRS precipitation is too
- widespread in both high latitudes
- and the tropics.
- Calibrating to AMSR-E greatly
- reduces spatial coverage; rates
- are boosted closer to AMSR-E.
- Mapping via histogram-matching
- can be extreme, due to limited
- range of AIRS rain rates
- (max. ~1.2 mm/hr).

**3. CALIBRATION WITH AMSR-E (cont.)**

## High-latitude Northern Hemisphere

- summer case, with precipitation
- near the center of the swath

- 26 July 2006, 04:27-05:10 UTC

- Original AIRS precipitation is too
- light and probably too widespread.
- Calibrating to AMSR-E reduces
- spatial coverage, but overestimates
- in the heaviest rain areas.
- Histogram-matching cannot “create”
- precipitation where it did not exist
- (e.g., the AMSR-E “tail”).

**3. CALIBRATION WITH AMSR-E (cont.)**

## with the other satellites in the A-Train constellation.   

**4. COMPARISONS WITH CLOUDSAT**

- Launched in 2006, CloudSat, with its Profiling Radar, flies in formation
- with the other satellites in the A-Train constellation.

- CloudSat views on a nadir track, providing a “curtain” of cloud/precip data at all latitudes.

## CloudSat Profiling Radar

- (41.0°S, 16.8°E)

- (61.7°S, 7.3°E)

- 10 July 2006, ~00:00-00:06 UTC

- The A-train formation allows for
- nearly-coincident space/time match-ups
- between AIRS relative humidity profiles
- and CloudSat radar reflectivities.

- Good correspondence between
- high/saturated deep-layer AIRS RH
- and high/deep CloudSat reflectivities
- in the heart of the storm system.

**4. COMPARISONS WITH CLOUDSAT (cont.)**

*However...AIRS quality flags must be*
*taken into account.*

## CloudSat Profiling Radar

- )

- (76.1°N, 5.1°W)

- (56.7°N, 30.8°W)

- 26 July 2006, ~04:24-04:30 UTC

- Excellent agreement between
- AIRS humidity profile and nearly-
- coincident CloudSat reflectivities,
- both in precipitating and non-
- precipitating clouds.

**4. COMPARISONS WITH CLOUDSAT (cont.)**

- Once again, quality flags indicate a
- fall-back to the microwave scheme in
- deep clouds, where CloudSat indicates
- precipitation.

## purple =	fall-back (MW)  

- 26 July 2006
- 04:30-04:36 UTC

- AIRS fields-of-view closest
- to the CloudSat track are
- depicted in:
- green =	good quality (IR)
- purple =	fall-back (MW)

**4. COMPARISONS WITH CLOUDSAT (cont.)**

## 5.	SUMMARY

- -	Since May 2005, GPCP V.2 monthly and daily products use AIRS in place of 		TOVS, after calibration to minimize discontinuities
- -	We are exploring methods to build on the Susskind et al. (1997) AIRS 		precipitation algorithm in preparation for:
- 			• a global version of the TMPA
- 			• GPCP V.3
- -	Calibration of AIRS to AMSR-E reduces unrealistically-high spatial coverage
- 	while boosting artificially low rates
- -	A-train constellation lends itself to comparisons amongst AIRS, AMSR-E,
- 	and CloudSat
- -	AIRS humidity profiles agree quite well with CloudSat Profiling Radar
- 	reflectivity profiles
- -	AIRS quality flags indicate fall-back to MW scheme in deep clouds,
- 	suggesting potential need to develop separate improvements in these areas

## 6.	FUTURE WORK

- -	Analyze recently-obtained quantitative precipitation estimates from CloudSat PR 	from CSU (L’Ecuyer)
- -	Develop a calibration for CPR against AMSR-E at mid-latitudes (ocean only
- 	at first?)
- -	At high latitudes, where AMSR-E is unable to estimate due to sea ice/cold
- 	surface, use the calibrated CPR to, in turn, calibrate AIRS
- -	Possibly incorporate MODIS data and/or other AIRS parameters
- -	Ultimately...implement improved AIRS precipitation algorithm into:
- 		• a global version of the TMPA
- 		• GPCP V.3

## 2. THE TOVS/AIRS ALGORITHM (cont.)

**2. THE TOVS/AIRS ALGORITHM (cont.)**

## purple =	fall-back (MW)  

- 22:30-01:30 UTC

- AIRS fields-of-view closest
- to the CloudSat track are
- depicted in:
- green =	good quality (IR)
- purple =	fall-back (MW)

**4. COMPARISONS WITH CLOUDSAT (cont.)**