# Presentation: 170193

## Potential Improvements to the AMSR-E L2 Rain over Land Algorithm*

- Ralph Ferraro and Arief Sudradjat
- NOAA/NESDIS, College Park, MD
- Cooperative Institute for Climate Studies, College Park, MD

- *Portions of this talk were presented at the 3rd TRMM Scientific Conference, February 2008

## Outline

- GPROF/land algorithm history
- Current status/evaluation
- Areas for improvement & possible solutions
- Screening and standardization of flagging
- Diversify the land data base and better utilization of Bayesian retrieval
- Convective classification (warm season bias)
- Anomalous surfaces:
- Semi-arid land & deserts
- Coastlines/small water bodies
- Snow within convective cells
- Summary

## GPROF-Land Background*

- 2A12 V4
- GSCAT
- 2A12 V5
- Retrofit/Engineer/Force/Replicate... NESDIS algorithm within GPROF
- Find profiles that match 22V-85V in NESDIS “calibration” relationship
- 2A12 V6 (current)
- Address deficiencies in unrealistic database and NESDIS relationship through match ups with PR and TMI
- Convective/Stratiform relationships
- Improved overall performance in terms of bias and correlation
- (Improvement) coastline rainfall
- 2A12 V7 (next version)
- Revisit some basic principles
- Will be carried out under auspices of both AMSR-E and PMM (focus on HF/cold season)

- *Easier to explain versions in terms of TRMM 2A12 product

## Examples of Good Results...

- Produces realistic rainfall fields when compared with radar, gauges, climatology, etc.
- GPROF-land is used in virtually all “blended” rainfall products (3B42, CMORPH, CHOMPS, etc.)

**Bottom two plots**
**Dave Wolff, GSFC**

## Examples of Problems...

- Regional biases
- Compared to PR
- Compared to gauges
- Unrealistic rain rate PDF’s in various regions?
- “Strange” conditional rates
- Underlying surface
- Systematic biases. Why?
- Unrealistic profiles in database
- Bias towards tropical, _ocean_ type systems
- Not enough precip. regimes
- Other “dirty laundry”
- Surface snow
- Inland water bodies

- Liu and Zipser

## Some “old” questions...

- Complex, outdated, “engineered” screening
- How did we get here?
- Developed for SSM/I
- Different FOV size, etc.
- Confusion over zero rain and indeterminate rain.  Example:
- Limitations over cold, dry land.  Is this an indeterminate rain rate or assumed to be zero?
- Surface ε effects
- Evaporation in dryer climate regimes
- Vastly different ice scattering to rain rate relationships

-       ! 11/02 Set pixel_status to invalid surface type and the
-       !       surface rainrate its negative value for ambiguous
-       !       rain retrievals
-       ! 03/03 Assigned covect the probable convective rain.  If type of rain
-       !       cannot be determined, convect is left as missing.
-       ! 09/03 Pixel_status is no longer set to -21 for ambiguous rain_status
-       !       All retrieved fields are set to their negative value for
-       !          pixels with an ambiguous rain_status
-       ! 03/04 All retrieved fields for a pixel with an ambiguous rain_status
-       !          are no longer set to their negative value

- Courtesy of G. Huffman

## Improvement Philosophy

- We will not be developing “quick fixes”
- Experience shows its not real effective...
- Look at GPROF improvements across ALL platforms
- GPM era
- Need to think “out of the box”
- “Things get old, fast”
- Past screening concept now archaic
- Relied on SSM/I and discriminant functions
- Did not want to rely on “others”
- Radiometer only
- Data production and delivery reliability
- Lack of experience in the user community
- Synergy with other data centers/research groups

## Areas for Improvement

- Screening
- “Modernize”
- Standardization of use of flags
- GPM era, need commonality across platforms!
- Diversify the land data base & better utilization of Bayesian retrieval
- Land systems, all seasons
- Emissivity models
- All useable measurements
- Convective classification (warm season bias)
- Anomalous surfaces
- Semi-arid land & deserts
- Coastlines/small water bodies
- Snow within convective cells

## Confused yet?

- Warm 85H,cold 22V  *MASKO*
- Other 85H,cold 22V
- Grody polarization  *MASKP*
- Ice polarization  *MASKP*
- Arid polarization  *MASKP*
- Not ambiguous  *0*

- Grody cold sfc.  *MASKI*
- Grody polarization  *MASKP*
- Ice polarization  *MASKP*
- Arid polarization  *MASKP*
- “missing” in 5x5  *33*
- Low sd
- Intermediate sd  *13*
- Not ambiguous  *0*

- Really cold sfc.  *MASKI*
- Ambiguous  14

**Current GPROF Screening**
- Courtesy of G. Huffman/GSFC

- Compute sd[T(85h)]

- After this, screening code tries to resolve the ambiguous regions.
- Non-scattering zero-rain flags (-51, -61) aren’t treated.
- Regions of positive (ambiguous) flags are evaluated for surrounding flag values:
- If perimeter is all negative, the area is set to the first negative found;
- if perimeter is zero and negative, the area is set to 13;
**if perimeter is zero, the area is set to *****0*****.**
**Unresolved ambiguous values are set to *****-15*****.**
- After this, “enough scattering” is checked in the rain code.

- MASKS = -21
- MASKP = -41
- MASKI = -31
- MASKO = -51
- MASKC = -61

## Standardization of Flags

- Current flags were designed to:
- “Diagnostics” for algorithm developers
- Geared for limited set of “early” users
- GPCP - Key concern was zero rain vs. indeterminate rain
- New, desired attributes include:
- Proper survey of user community of their needs
- A nomenclature that everyone can understand
- A set of flags that is useful for everyone
- Commonality across all sensors in GPM era
- Will be handled through PMM

## New Approaches for Sfc. Identification

- 85 GHz Emissivity

- Through utilization of both operational and research data sets that
- are routinely available, we should be able to conquer the land surface
- screening problem with relative ease.

## Convective/Stratiform

- Current method (a combination of 3 published methods) was improvement in V6 from V5, but still has problems.  This is the culprit of the warm season bias.
- Why?
- Resolution – TMI is coarser than PR?  Spreads out rain?
- Beam filling?  Underestimate intense rain

## GPROF databases and Rain Retrieval

- Examine and improve land databases
- GPROF land profiles are _limited_ in diversity
- Predominantly from tropical oceanic systems
- Not diverse nor realistic enough for true physical retrieval
- Look at excellent data sets from a variety of sources
- C3VP, CloudSat, NOAA’s HMT (West Coast U.S./Winter)
- Expand previous TMI/PR studies with full 10-year data
- Working with PMM science team members...
- Retrieval needs to expand to more than just 22V-85V
- Published studies show other combinations are better in different precipitation regimes

## Special (Seasonal) Situations

- False snow (ambiguous) inside of thunderstorm:
- Usually the heaviest RR’s in storm system
- V6 attempts to filter these out, but it doesn’t always work
- Why? Ice scattering at 22V (rare); similar characteristic to snow cover
- Solution  use of snowcover climatology and land surface temperature
- Arid regions (e.g., Sahel)
- Screening removes potential rain areas BUT...
- Also isn’t 100% effective, so false alarms result
- Also removes true rain in other regions
- False alarms create unrealistic conditional rain rates
- Solution better utilization of emissivity calculations and surface type information
- What’s going on with small water bodies?
- Is it called coast or land?
- Applying the wrong algorithm to the wrong surface type? OR
- Is it a mixed pixel effect?
- Does it have seasonal effects due to vegetation cover?
- Impact is at the low end (onset) of rainfall
- Solution Examine current land/sea/coast mask as a starting point...

## AMSR-E – L, W, C

- Impact of “thick” coast
-  on retrievals?

- Are inland water bodies important?
- Coastline “improvement” not intended here!

## Preliminary investigation – Rain/No Rain

- Global

- Africa

- Grody Rule: Rain if TB22V>264 K and TB22V>175+.49*TB85V
- GPROF uses TB21V-TB89V>5 K + Grody rule + GSCAT rule + spatial filter

- Snow
- Cover

- Rain

- No
- Rain

- Rain

- Snow
- Cover

- No
- Rain

## Preliminary investigation – Desert/Semi-Arid

- Global

- Africa

- No rain if PD19>20 OR TB85>253 K and PD19>7

- ARID

- DESERT

- DESERT

- ARID

- DESERT

- ARID

## Summary

- GPROF-land is utilized in a number of applications and has proven useful
- Despite this, feedback of current version by the user community suggest several critical issues that need to be resolved for next upgrade
- We will try to prioritize which problems we can solve
- Looking towards more generic fixes for GPM era rather than band aids for TRMM and AMSR-E...
- PMM Team Meeting (August 2008) plans to address some of these through WG’s:
- Land surface/emissivity WG (Gail S-J./Christa P-L./Ralph F.)
- Rain detection WG (Guosheng L./Grant P.)

## Backup Slides

## NOAA HMT 2005 

- T and q  profiles from HMT RAOB
- and current land database

- HMT S-band  radar reflectivity
- Freezing level is around 2.5 km
- Database lacks such profiles

## Courtesy of M. Diederich

- TRMM

- AMSR-E

- SSM/I

## High Resolution Land/Water Mask

## Filtering out smaller water bodies

| Year ->
 | 02 | 03 | 04 | 05 | 06 |
| --- | --- | --- | --- | --- | --- |
| AMSR-E (old the one we used for previous OI) |  |  |  |  |  |
| AMSR-E (what we have now) |  |  | Same as above | Same as above | Same as above |

**B02   **2002-06-18 (23:40) to 2004-09-24(14:04)

**B03**   2004-09-24(14:04) to 2004-11-04(00:46)

**B04  **2004-11-04(00:46) to 2005-04-01(4:28)

**B05  **2005-04-01(4:28) to 2005-07-06 (4:28)

**B06**  2005-07-06 (4:28) to 2005-08-23 (4:27)

**B08  **2006-03-10 (9:31) to 2006-11-13 (02:58)

**B07**  2005-08-23 (4:27) to 2006-03-10 (9:31)

| Year-> | 07 |
| --- | --- |
| AMSR-E (old the one we used for previous OI) | From 2006-11-13 to end of year v9 stage to algorithm (this is the latest and all the data is being reprocessed with this)   |
| AMSR-E (what we have now) | From 2006-11-13 to end of year v9 stage to algorithm (this is the latest and all the data is being reprocessed with this) |

**v9   **2002-06-18 (23:40) to 2003-12-31(23:59)

## Slide 25

|  | AMSR-E  Version Descriptions |
| --- | --- |
| B02 
(2002-06-18 23:40)  | The number of pixels per scan changed from 392 to 486 (the full swath width).  |
| B03 (2004-09-24 14:04) | Corrects low tropical coastal rainfall retrievals and modifies calculations of cold season coastal rainfall estimates to eliminate false rain signatures, which also eliminates most true rainfall signatures for this condition. The algorithm also eliminates rainfall retrievals over ocean when the sun glint is greater than 20 degrees. In AE_RnGd only, the B03 algorithm lowers the upper limit for acceptable brightness temperatures over ocean, and corrects minor errors in the processing code.  |
| B04 (2004-11-04 4:28) | Corrects errors resulting from the AMSR-E 89 GHz-A feedhorn failure.  |
| B05 (2005-04-01 4:27) | Improves low coastal rain rates. |
| B06 (2005-06-02 4:28) ??? | Rain rate is now calculated for surface types of "possible ice over coast |
| B07 (2005-08-23 4:27) | Update L2A reader for version 5 L2A. |
| B08 (2006-03-10 9:31) | Updates to accommodate the L2A version B07 format changes. |