# Presentation: 050117

## Assimilation of MODIS and AMSR-E Land Products into the NOAH LSM

**Xiwu Zhan****1****,** **Paul Houser****2 ****, ****Sujay Kumar****1**
**Kristi Arsenault****1****, Brian Cosgrove****3**
*1** UMBC-GEST/NASA-GSFC; **2** GMU/CREW; **3** SAIC/NASA-GSFC*

**JCSDA 3****rd**** Workshop on Satellite Data Assimilation**

- Project Rationale
- Objectives
- Progress
- Plan

**OUTLINE**

## RATIONALE

- Land surface information improves weather and climate prediction;
- Near-real-time land observations (MODIS, AMSR-E) are available;
- Few satellite land products are used in operational weather and climate prediction;
- Lack of proven operational land assimilation methods have been a limit;
**GOAL:** Implement Kalman Filter to assimilate land satellite data products into the Noah land surface model installed in the Land Data Assimilation Systems (NLDAS/GLDAS)

## OBJECTIVES

- Identify relevant MODIS & AMSR data products;
- Implement the Kalman Filter in LDAS/LIS;
- Examine the efficiency and benefits of assimilating the satellite data products into the NOAH LSM.

**PROGRESS**

- Satellite data products selected;
- Data assimilation technique implemented;
- Results of soil moisture data assimilation;
- Results of using MODIS land cover data;
- Results of using other data products.

## Satellite Data Products to be Assimilated

- AMSR-E SM/TB: top layer SM/TB observed, 4 layer Noah SM updated with Kalman filter DA;
- MODIS land cover: replace AVHRR with MODIS LC;
- MODIS snow cover: nudging model snow cover/depth/ SWE with MODIS and in situ (SnoTEL) snow data;
- MODIS LST: update 4 layer soil temperature with MODIS LST using Kalman filter DA; could also use GOES LST?

## Data Assimilation Techniques in LIS

- Direct Insertion (DI): replace LSM states with corresponding observation data;
- Kalman Filters (EKF/EnKF): correct LSM states by weighing model forecasts and observations with their error covariance:
***Xa = Xb + K [Z – h(Xb)],***
***K = PHT/[HPHT + R].***
- Land Information System: LIS (enhanced NLDAS/ GLDAS software system) includes plug-ins for both DI and EKF; This plug-in system design allows assimilating any state variable data using any LSM; The EnKF is being implemented using the plug-in system design and an ensemble generation algorithm recently developed.

## Soil Moisture Data Assimilation in LIS

- LIS-EKF: The Extended Kalman Filter is implemented in LIS to assimilated TMI 0-2cm soil moisture retrievals of the SGP’99 area into the Mosaic and Noah land surface models;
- SGP’99 TMI SM: Jackson & Hsu (2001) retrieved and validated 0-2cm SM for an ~140km by 280km area in central OK for 14 days from July 8 to 21, 1999;
- SMEX’02 SM: SM for SMEX’02 area simulated with Noah LSM in LIS compared with in situ observations;
- AMSR-E SM: B01 version retrieval algorithm was used before Feb 15, 2005. B02 algorithm is used on an after that. B01 uses 10.7GHz TBs only while B02 uses both 6.9 and 10.7 GHz TBs.

## SGP’99 TMI SM Data Assimilation with Mosaic LSM

- TMI

- DI

- EKF

- Wet
- Start

**SGP’99 TMI SM Data Assimilation with Mosaic LSM**

## SGP’99 Latent Heat Flux from Mosaic LSM

- No
- DA

- DI

- EKF

- Wet
- Start

**SGP’99 Latent Heat Flux from Mosaic LSM**

## SGP’99 TMI SM Data Assimilation with Mosaic LSM

*No DA*
*DI*
*EKF*
*TMI Obs*

**o**

*Wet start**, 0-2cm Layer*

**SGP’99 TMI SM Data Assimilation with Mosaic LSM**

- For wet start case, KF DA advantage is more significant.

## SGP’99 TMI SM Data Assimilation with Mosaic LSM

*Wet start**, 2-148cm Layer*

*No DA*
*DI*
*EKF*

**SGP’99 TMI SM Data Assimilation with Mosaic LSM**

- KF DA uses the correlations between the different soil layers in the Mosaic LSM.

## SGP’99 TMI SM Data Assimilation with Noah LSM

**SGP’99 TMI SM Data Assimilation with Noah LSM**

*Dry start**, 0-10cm Layer*

*No DA*
*DI*
*EKF*
*TMI Obs*

**o**

- SM DA with Noah LSM needs special treatment for using 0-2cm SM obs to update 0-10cm top soil layer SM of the LSM
- Directly using 0-2cm SM for the 0-10cm SM of Noah LSM may be misleading.

## SGP’99 TMI SM Data Assimilation with Noah LSM

*Dry start**, 10-40cm Layer*

**SGP’99 TMI SM Data Assimilation with Noah LSM**

*No DA*
*DI*
*EKF*

- Second layer SM of Noah LSM did not get updated;
- There is no SM correlation between the Noah LSM soil layers? Or
- The current code of either EKF or Noah LSM has bugs?.

## SMEX’02 SM Simulations with Noah LSM

- 0-1cm

- 1-6cm

## SMEX’02 SM Simulations with Noah LSM

## AMSR-E Surface Soil Moisture Retrievals

- Version B00

- Version B01

- Version B02

- AMSR-E SM algorithm changes;
- Newest algorithm starts on 2/15/05;
- Some areas do not have retrievals;
- Will be assimilated at 0.25° grids globally;
- May directly assimilate TB data if retrievals are suspect.

## Impact of MODIS LC Data on Noah LSM Simulations

- _AVHRR_
- UMD land cover

- _MODIS V4_
- UMD land cover

- _MODIS V3_
- UMD land cover

- Rio Grande River Basin in New Mexico Below Elephant Butte Dam

- Arsenault et al. 2005

## Arsenault et al. 2005

- _Latent Heat Flux (W m__-2__)_

- _Top 10 cm Soil Temperature (Celsius)_

- _Sensible Heat Flux (W m__-2__)_

_**Differences**_** between  (1) AVHRR run and (2) MODIS-V3**
**May 30, 2002 (18 Z)**

- These figures show the differences in latent heat flux, sensible heat flux and the top layer soil temperature for the Noah LSM.

## Albuquerque, NM area – May 30, 2002 (18Z)

_**Albuquerque, NM area – May 30, 2002 (18Z)**_

_**Latent Heat Flux (W m-2):  AVHRR run – MODIS3 run**_

## LDAS LSM and MODIS Snow Cover Comparison

- Central Washington State – Yakima Basin (February 24, 2003)

-  Comparison between the LDAS LSMs snow cover fields and Terra MODIS daily snow cover extent.  (Purple indicates ***snow***, yellow is ***clouds***, and  beige is ***snow-free land***.)
-  Noah LSM underestimates and CLM2 overestimates snow cover when compared to the MODIS 1-day and also 8-day fields for most of the winter (for WY2002-2003).
-  In later spring months when snow melt occurs, the model snow cover has been found to identify snow in locations that MODIS algorithms fail to locate the snow beneath the tree canopies, when compared to in-situ measurements.

**Noah 2.7.1 LSM**

**CLM2 LSM**

**MODIS 1-Day**

## Impact of Assimilating MODIS Snow Cover Data

**Impact of Assimilating MODIS Snow Cover Data**

**21Z 17 January 2003**

**Control Run Mosaic SWE (mm)**

**Enhanced MODIS Snow Cover (%)**

**Assimilated Mosaic SWE (mm)**

**IMS Snow Cover**

**SNOTEL and Co-op Network SWE (mm)**

**Mosaic SWE Difference (mm)**

- Rodell et al., 2003

## FOLLOWING YEAR PLAN

- Assimilate global 0.25 AMSR-E SM retrievals and TB observations into LIS-Noah LSM using the EKF-implemented LIS;
- Implement the Ensemble Kalman Filter in LIS;
- Assimilate MODIS LST into LIS-Noah LSM using the EnKF-implemented LIS;
- Assess and publish the efficiency/benefits of assimilating MODIS LC, LST, snow cover, and AMSR-E SM/TB.

| Year | Task 1 | Task 2 | Task 3 |
| --- | --- | --- | --- |
| 1 | Data Identification, QC, and Importation  |  |  |
| 2 |  | EKF/EnKF DA and software development  |  |
| 3 |  |  | DA Evaluation, impact studies, and code transfer  |