# Presentation: 222547

## TRMM Ground ValidationSome Lessons and Results

*David B. Wolff, David Marks, David Silberstein & Richard Lawrence*
*TRMM Satellite Validation Office*
*NASA/GSFC*

## TRMM GV Data Processing is up-to-date

Major issues are gauge data problems and (especially at KWAJ) radar calibration uncertainties.

GSFC is developing an automated method of correcting relative calibration of the KWAJ radar to salvage historical KPOL data.

Comparisons of GV estimates to TRMM (3G68) show good agreement, when radar calibration is not major issue.

Comparisons of GV estimates to other datasets (e.g. MPA) also show good agreement and bode well for GPM era sampling expectations

***Major issues are gauge data problems and (especially at KWAJ) radar calibration uncertainties.***
***GSFC is developing an automated method of correcting relative calibration of the KWAJ radar to salvage historical KPOL data.***
***Comparisons of GV estimates to TRMM (3G68) show good agreement, when radar calibration is not major issue.***
***Comparisons of GV estimates to other datasets (e.g. MPA) also show good agreement and bode well for GPM era sampling expectations***

*Summary*

## Calibration is a Major Issue for TRMM GV

Houze et al. (2004) cite the error associated with a ± 2 dB calibration error is ± 30%,  in rain rate, respectively.

Actual calibration uncertainty at KWAJ frequently well over ±2 dB and sometimes > 10 dB!

Absolutely critical that proper calibration procedures be in place for GPM.

Calibration uncertainty is the single largest source of error in Kwajalein rainfall estimates.

While absolute calibration is not always possible using conventional radars, stable calibration is essential.

GSFC is developing a method to correct historical calibration uncertainties using probability distributions of clutter area reflectivity.

*Houze et al. (2004) cite the error associated with a ± 2 dB calibration error is ± 30%,  in rain rate, respectively.*
*Actual calibration uncertainty at KWAJ frequently well over ±2 dB and sometimes > 10 dB!*
*Absolutely critical that proper calibration procedures be in place for GPM.*
*Calibration uncertainty is the single largest source of error in Kwajalein rainfall estimates.*
*While absolute calibration is not always possible using conventional radars, **stable** calibration is essential.*
*GSFC is developing a method to correct historical calibration uncertainties using probability distributions of clutter area reflectivity.*

*Importance of Stable Calibration*

## GV v5: Kwajalein Monthly Radar/Gauge Rainfall Statistics

## Detrimental Effects of Unstable Radar Calibration

*Detrimental Effects of Unstable Radar Calibration*

*Waveguide change occurred on or around July 1, 2003*

## TRMM GV uses a “clutter-map” in quality control step at KWAJ.

*TRMM GV uses a “clutter-map” in quality control step at KWAJ.*

* **This map provides a lookup *
*table for areas (r, ****) that have a *
*high probability of echo when *
*no precipitation is present.*
* **Given KPOL resolution, there are ~4000  points per VOS.*
*Calculate daily PDF of Clutter Area Reflectivity (CAR).*
*Fond that upper percentiles (e.g. 95th) are remarkably stable **when the radar calibration is stable**, even when precipitation is present.*

*Statistical/Automated Correction of Calibration*

## Statistical/Automated Correction of Calibration

*Statistical/Automated Correction of Calibration*

*Calibration changes are often traceable to engineering changes to the radar system... here a +7 dB gain was applied.*

## Statistical/Automated Correction of Calibration

*Statistical/Automated Correction of Calibration*

*95**th** Percentile Reflectivity in Clutter Areas is Stable*

*Silberstein et al. 2005 (32**nd** Radar Conference)*

## Relative Calibration Adjustment (RCA)

*RCA vs. Engineering Changes*

- http://trmm-fc.gsfc.nasa.gov/trmm_gv/gv_products/GVproducts.html

## Statistical/Automated Correction of Calibration

*Statistical/Automated Correction of Calibration*

*UW estimates based on*
*comparison to TRMM PR*
*(Houze et al. 2004)*

## Quantifying RCA Effects vs. Z-R Changes

*Marks et al. 2005 (32**nd** Radar Conference)*

*V5:  2002 WPMM, no RCA*
*V6: Seasonal WPMM, RCA*

## V5 Biases - KWAJ 1999-2004 GV vs. TRMM

## V5 Monthly Means - KWAJ 1999-2004

## V6r Biases - KWAJ 1999-2004

## V6r Monthly Means - KWAJ 1999-2004

## A similar comparison was done using the Multi-Satellite Precipitation Analysis (3B42) Rain Estimate (Huffman et al. 2005)
0.25° x 0.25° gridded product, available every 3-hours
GV gridded similarly
Compared the 3-hour accumulations from both GV and MPA over these pixels.

*0.25° x 0.25° gridded product, available every 3-hours*
*GV gridded similarly*
*Compared the 3-hour accumulations from both GV and MPA over these pixels.*

*Comparing GV to Other Rain Estimates (MPA)*

## KWAJ: Comparisons to Other Datasets (MPA)

*KWAJ: Comparisons to Other Datasets (MPA)*

## MELB: Comparisons to Other Datasets (MPA)

*MELB: Comparisons to Other Datasets (MPA)*

## Summary

TRMM GV data processing is current for all sites

Calibration issues at Kwajalein a major source of error

Relative Calibration Adjustment (RCA) shows promise but is still a work in development.

Comparisons to TRMM over MELB generally within ± 10% on year-to-year basis

Comparisons to other estimates (MPA) also show good agreement in MELB and KWAJ (during period when radar calibration is mitigated).

TRMM GV is providing GPM GV a significant number of  “lessons learned”, which are being applied in GPM GV development

*TRMM GV data processing is current for all sites*
*Calibration issues at Kwajalein a major source of error*
*Relative Calibration Adjustment (RCA) shows promise but is still a work in development.*
*Comparisons to TRMM over MELB generally within ± 10% on year-to-year basis*
*Comparisons to other estimates (MPA) also show good agreement in MELB and KWAJ (during period when radar calibration is mitigated).*
*TRMM GV is providing GPM GV a significant number of  “lessons learned”, which are being applied in GPM GV development*

## Backup Slides

## Statistical/Automated Correction of Calibration

*Statistical/Automated Correction of Calibration*

*Diurnal Differences in 95**th** Percentile Clutter Reflectivity*

## Comparison of Rain Intensity Distributions

- TRMM -->  global (land or ocean) for Feb 1998 (ITE110 - official V6, thanks to S. Yang)
- GV -->  KWAJ (Ocean) and MELB (Land) for period 07-12/1999
- Over ocean all PDFs nearly log-normal with some exceptions.
- Over land, there remains work to be done, especially for 2A12 (TMI).

*Comparison of Rain Intensity Distributions*

## TRMM GV Processing Status

## Statistical/Automated Correction of Calibration

_*Benefits of “clutter map” approach:*_
*95**th** percentile clutter-area reflectivity is remarkably stable **even in the presence of precipitation**. Little or no diurnal effect*
*Easily calculated for near-real-time monitoring of the current state of the relative radar calibration*
*Current effort: determine the best means of applying these relative calibration changes to historical data to develop improved GV products*
*Signal is strong, amplitude is in question. Under further investigation.*

## Version 6 GV Site Surface Masks

*Version 6 GV Site Surface Masks*

## 3G68 & GV Land/Coast/Ocean Masks

*3G68 & GV Land/Coast/Ocean Masks*

## Notes on Bias Calculations

Two references (GV and Satellite)
GV_Ref = Mean 0.5° GV rain intensity
Sat_Ref = (PR + TMI + COM)/3

Bias = (Reference - Measurement) / Measurement

***Two references (GV and Satellite)***
*GV_Ref = Mean 0.5° GV rain intensity*
*Sat_Ref = (PR + TMI + COM)/3*
***Bias = (Reference - Measurement) / Measurement***

*Comparing GV and TRMM Data*

## TRMM 3G68 (gridded PR, TMI and COM) rain intensity estimates compared to similarly gridded TRMM GV estimates. All TRMM estimates are Version 6!

0.5° x 0.5° over land, coast and ocean

Analysis of 1999-2004 TRMM-v6 has been completed

*0.5° x 0.5° over land, coast and ocean*
*Analysis of 1999-2004 TRMM-v6 has been completed*

*Comparing TRMM and GV Rain Intensities*