# Presentation: 474451

## Global Microwave Surface Emissivity Error AnalysisPI: Dr. Andrew S. JonesMr. Phil Shott, Mr. John Forsythe,Ms. Cindy Combs, Mr. Matt Nielsen,Dr. Phil Stephens, Mr. Ron Kessler,Dr. Tomislava Vukicevic,	Dr. Thomas H. Vonder Haar CSU/CIRADoD Center for Geoscience/Atmospheric Research (CG/AR)

## Goals and Objectives

- Conduct error analysis work related to the Microwave Emissivity Model (MEM)
- Generalize the error characterization approach to future NESDIS/NWS needs

## Introduction / Outline

- Start date: Sep. 1, 2003now in 2nd YR
- AMSU-B Antenna Pattern Corrections
- Emissivity Case StudySeptember 2003
- Results
- Preliminary Conclusions
- Discussion

- Overview

## Accomplishments

- Validated AMSU-B APC method
- ~10-15% reduction in upper-level WV retrieval bias
- Fully portable module for JCSDA systems
- Global AMSU Emissivity Results
- Emissivities are optimized in band intervals at:23, 31, 50, 89, and 150 GHz
- Near-real-time emissivity retrievals are possible
- Path to NESDIS Operations
- The host system, “DPEAS”, is scheduled to transition to operations at OSDPD in FY05

- Key
- Results

## AMSU-B APC 1DVAR ImpactsOcean 1DVAR Retrieval Validation

- AMSU-B
- APC

| Retrieved Variable | Mean Retrieval Difference
(with APC – without APC)
(variable units) | Mean
Difference
(%) |
| --- | --- | --- |
| Surface temperature | 0.52 K | 0.1 |
| 23 GHz emissivity
31 GHZ 
50 GHz 
89 GHz
183 GHz | -0.007 
-0.007 
-0.007 
-0.007 
-0.007 | -1.4
-1.3
-1.3
-1.1
-0.9 |
| 300 hPa mixing ratio
500 hPa
700 hPa 
850 hPa
1000 hPa | 0.002 g/kg
0.013 g/kg
0.061 g/kg
0.430 g/kg
0.790 g/kg | 10.6
14.8
9.3
6.9
5.7 |
| 300 hPa temperature
500 hPa 
700 hPa 
850 hPa 
1000 hPa | 0.35 K
0.37 K
0.41 K
0.34 K
0.32 K | 0.2
0.1
0.1
0.1
0.1 |

- greatest
- impact

- noticeable
- impact
- over
- oceans

## 1DVAR Input Fields:Microwave Satellite Data

- Emissivity
- Retrieval
- Input

**Example:** 89 GHz
- AMSU-B Chan 16
- Brightness Temperature
**Key dependencies:**
- 1) ocean/land contrast
- 2) scan position /zenith anglesrotated polarizations
- 3) ascending/descending

## 1DVAR Input Fields:AGRMET / GDAS / MEM Model Data

- Emissivity
- Retrieval
- Input

**GDAS q (700 hPa)**

**MEM emis (89 GHz)**

**AGRMET LST**

- 58 Model Input Fields
- 22 Model Diagnostic Input Fields
- 96 Satellite Input Fields	(Ta, Tb, Goldberg AMSU-A      retrievals, MSPPS products)
- 384 Retrieval Output Fields
**560 Total Fields @ ~1MB each**

- 12h composite
- results are shown
- Actual processing is
- by one orbit granules

## 1DVAR Results: Emissivity89 GHz Mean Emissivity

**12h Composite:**
**89 GHz Emissivity**
**Precip. Filter:**
**MSPPS rainrate**
**Gray represents areas**
**with ******** < 0.7 or**
**no data for 12h composites**
**(see color bar below)**

**sea ice edge**

- Emissivity
- Retrieval
- Output

**1DVAR Results: Emissivity****89 GHz Mean Emissivity**

## 1DVAR Results: EmissivityTime Series

- Retrieval
- Output

**10 Day Time Series of**
**12h Composites**
**Start: 00:00 UTC**
**89 GHz Emissivity**

**1DVAR Results: Emissivity****Time Series**

## 1DVAR Results: EmissivityZenith Angle Series

- Emissivity
- Retrieval
- Output

**10 Day Composites**
**2003 264-273**
**89 GHz Emissivity**
**Zenith Angle Series:**
**   ****0-10    degrees**
** ****10-20          ”**
** ****20-30****	****	****  ”**
** ****30-40****	****	****  ”**
** ****40-50****	****	****  ”**
** ****50-60****	****	****  ” **

## 1DVAR Results: EmissivityFrequency Series

- Emissivity
- Retrieval
- Output

**10 Day Composites**
**2003 264-273**
**Emissivity Series:**
**  ****23 GHz**
**  ****31 GHz **
**  ****50 GHz**
**  ****89 GHz**
**150 GHz**
**Land/ice emissivity results**

## 1DVAR Results:Data Variance Contribution (%)Frequency Series

- Retrieval
- Output

**Emissivity variance**
**due to AMSU**
**input data**
**100% = relies only on****AMSU data**
**0% = relies only on****background**
**150 GHz saturates****in the tropics**

**1DVAR Results:****Data Variance Contribution (%)****Frequency Series**

## MEM IntercomparisonFrequency Series

- MEM
- Validation
- Activities

**10 Day Composites**
**2003 264-273**
**Emissivity Series:**
**  ****23 GHz**
**  ****31 GHz **
**  ****50 GHz**
**  ****89 GHz**
**150 GHz**
****** ****= Retrieval - MEM**
**Biases are well above**
**sensor error**

## MEM IntercomparisonFrequency Series     (|| > 0.02)

- MEM
- Validation
- Activities

**10 Day Composites**
**2003 264-273**
**Emissivity Series:**
**  ****23 GHz**
**  ****31 GHz **
**  ****50 GHz**
**  ****89 GHz**
**150 GHz**
****** ****= Retrieval - MEM**

## MEM IntercomparisonZonal and Global Statistics(no-coasts and no-ice: only “land”)

- MEM
- Validation
- Activities

**10 Day Composites**
**2003 264-273**

## MEM IntercomparisonZonal and Global Statistics(no-coasts and no-ice: only “land”)

- Validation
- Activities

**10 Day Composites**
**2003 264-273**

_**freq./global mean**_
**150 GHz / ****	****-0.0078**
**23 GHz / ****	****-0.0160**
**31 GHz / ****	****-0.0171**
**89 GHz / ****	****-0.0179**
**50 GHz /****	****-0.0312**

**MEM Intercomparison****Zonal and Global**** ****Statistics****(no-coasts and no-ice: only “land”)**

## MSPPS Emis. IntercomparisonFrequency Series

- MEM
- Validation
- Activities

**10 Day Composites**
**2003 264-273**
**Emissivity Series:**
**  ****23 GHz**
**  ****31 GHz **
**  ****50 GHz**
****** ****= Retrieval - MSPPS**

**MSPPS emissivities**
**are in closer agreement**
**than the MEM**

## Good agreement (|| < 0.02) in ~13-21% of areas
All frequencies simultaneously in good agreement in ~5% of areas
Challenges for the MEM:
A strong positive MEM bias in deserts, and low veg. tundra,esp. in Sahara / Middle East at higher frequencies
Zenith angle dependence can be strong over deserts
Littoral and riverine areas are poorly represented
Local emis. differences can be substantial (|| > 0.1)
Source of 50 GHz bias?
Global avg. diff. is larger than other freqs. 50 GHz the lowest agreement: ~13%
50 GHz is the most T(p) bias-sensitive channel, however MSPPS intercomparisons suggest that the MEM may be the bias source. Question remains open.
Extensive additional validation work with 1DVAR temperature / water vapor retrieval profiling over land is in progress (DoD funded)
Challenges for the C1DOE emissivity retrieval:
150 GHz saturates in the tropics, additional cross-correlation studies are needed to leverage lower freq. emissivity info. to 150 GHz
Fairly limited case study results
Large computational requirements (~3-6 CPUs are needed for near real-time global results per satellite)

- Emissivity
- Retrieval
- Conclusions

- Good agreement (|| < 0.02) in ~13-21% of areas
- All frequencies simultaneously in good agreement in ~5% of areas
- Challenges for the MEM:
- A strong positive MEM bias in deserts, and low veg. tundra,esp. in Sahara / Middle East at higher frequencies
- Zenith angle dependence can be strong over deserts
- Littoral and riverine areas are poorly represented
- Local emis. differences can be substantial (|| > 0.1)
- Source of 50 GHz bias?
- Global avg. diff. is larger than other freqs. 50 GHz the lowest agreement: ~13%
- 50 GHz is the most T(p) bias-sensitive channel, however MSPPS intercomparisons suggest that the MEM may be the bias source. Question remains open.
- Extensive additional validation work with 1DVAR temperature / water vapor retrieval profiling over land is in progress (DoD funded)
- Challenges for the C1DOE emissivity retrieval:
- 150 GHz saturates in the tropics, additional cross-correlation studies are needed to leverage lower freq. emissivity info. to 150 GHz
- Fairly limited case study results
- Large computational requirements (~3-6 CPUs are needed for near real-time global results per satellite)

## AMSU-B APC bias adjustment
Simple “static” MEM bias adjustment to improve coastal, river, and desert MEM results
Dynamic near real-time emissivity product.
Improvements for changing snow/ice conditions...
Operational MEM quality monitoring...
Emissivity improvements to current microwave land surface product suite, and atmospheric products over land
Which additional MW sensors to target?

- Discussion

- AMSU-B APC bias adjustment
- Simple “static” MEM bias adjustment to improve coastal, river, and desert MEM results
- Dynamic near real-time emissivity product.
- Improvements for changing snow/ice conditions...
- Operational MEM quality monitoring...
- Emissivity improvements to current microwave land surface product suite, and atmospheric products over land
- Which additional MW sensors to target?

## For more technical info, references...

- http://lamar.colostate.edu/~asjones/Jones/default.htm
- Jones@cira.colostate.edu

## Antenna Pattern Corrections

- AMSU-B
- APC

**APC range is 0.25 K - 2.2 K**
**89 GHz is most affected**

**AMSU-B APC as a function of**
**view angle and Earth temperature****  **

## Antenna Pattern Corrections

- AMSU-B
- APC

**APC method intercomparison**
**for AMSU-A Channel 1 (23.8 GHz)**

- solid line
- position-dependent
- AMSU-A APC,
- after Mo (1999)

- mainbeam assumption

- position-independent
- widebeam assumption
- AMSU-B APC method
- emulating the AMSU-A
- APC for Chan 1
*Symbols represent*
*mean and*
*** **1 sigma range*

**general**
**agreement**
**using**
**widebeam**
**assumption**

**Full details are described in the AMSU-B APC manuscript draft:**
- Nielsen, M. J., P. J. Stephens, A. S. Jones, J. M. Forsythe, R. W. Kessler,T. H. Vonder Haar, 2005: AMSU-B Antenna Pattern Corrections.

## 1DVAR ApproachCIRA 1DVAR Optimal Estimator (C1DOE)

- AGRMET / GDAS first guess information for land surface and atmospheric state
- Control vector = (LST, 5 emissivity bands, q (p),T (p) for 7 atmospheric control levels)~40 RTM levels are carried internally
- Initial assumption: background emissivity band covariances are uncorrelated
- Loose instrument variances (7 K) until system biases are adjusted in future experiments(emis. differences > ~0.02 are significant)

- Emissivity
- Retrieval
- Method

## Improved Cross-correlation Computations
Various cloud filters over ocean/land(e.g., deviation from min. emissivity results,use of IR data sets)
Intercomparison to cloud-cleared IR/MW Emissivities (collaborative work with Drs. Ruston and Baker (NRL))

- Future
- Work

- Improved Cross-correlation Computations
- Various cloud filters over ocean/land(e.g., deviation from min. emissivity results,use of IR data sets)
- Intercomparison to cloud-cleared IR/MW Emissivities (collaborative work with Drs. Ruston and Baker (NRL))

## Global Microwave Surface Emissivity Error Analysis (Jones)Purpose:- Conduct error analysis work related to the JCSDA Microwave Emissivity Model (MEM)- Generalize the error characterization approach to future NESDIS/NWS needsProgress so far:- Created and validated the AMSU-B Antenna Pattern Correction module	    (results in 10-15% bias improvements to AMSU-B upper-water vapor profiles)- Created a robust near-real-time 1DVAR global emissivity retrieval system suitable for transition to operations using the DPEAS grid computing framework- MEM intercomparison to 1DVAR emissivity retrievals indicates several regions needing future MEM improvement (particularly desert and coastal regions) differences can be locally largeFuture Plans:- Transition the AMSU-B Antenna Pattern Correction module to operations- Continue emissivity cross-correlation studies and collaborations re: MEM improvements- Perform intercomparisons with NRL JCSDA emissivity work- From JCSDA needs, determine the future operational role of the dynamic global 1DVAR               emissivity retrieval system