# Presentation: 869860

## Biases and scaling in soil moisture and temperature data assimilation

- May 31, 2006
- Greenbelt, MD
***Biases and scaling in soil moisture and temperature data assimilation***
**R. H. Reichle****1,2**
**R. D. Koster****1**** **
**M. G. Bosilovich****1**
**S. P. P. Mahanama****1,2**
**	****1 – Global Modeling and Assimilation Office, NASA**
**	****2 – GEST, University of Maryland, Baltimore County**
**	**

## Outline

| Method | Ensemble Kalman filter |
| --- | --- |
| Soil Moisture | Data, biases, and assimilation |
| Soil Temperature | Data, biases, and assimilation |

## Land data assimilation

- Satellite *surface* soil moisture & temperature

- Observed precipitation, radiation

- Land model

- Model soil moisture & temperature

- “Optimal”soil moisture & temperature

***EnKF***

***Land data assimilation***

***Data assimilation with the Ensemble Kalman filter (EnKF):***
**Consider relative uncertainties in modeled and observed soil moisture.**

## Soil moisture assimilation

**x****k****i**  state vector (eg soil moisture)
**P****k**** ****   **state error covariance
**R****k****    **observation error covariance

***Soil moisture assimilation***

## Outline

| Method | Ensemble Kalman filter |
| --- | --- |
| Soil Moisture | Data, biases, and assimilation |
| Soil Temperature | Data, biases, and assimilation |

## Data sources

|  |  | “SMMR period”
1979-87 (~8.5 years) | “AMSR-E period”
2002-05 (~3.5 years) |
| --- | --- | --- | --- |
| Soil moisture retrievals | Sensor | SMMR (Nimbus 7) | AMSR-E (Aqua) |
|  | Frequency | C-Band (6.6 GHz) | X-Band (10.7 GHz) |
|  | Sampling depth | ~1.25 cm  | ~1 cm |
|  | Horiz. Resolution | ~150 km | ~40 km |
|  | Equator crossing | 12 am/pm | 1:30 am/pm |
|  | Algorithm | Owe et al., 2001 | Njoku et al. (http://nsidc.org) |
| Land surface model |  | NASA Catchment (~0.5°) | (same w/ minor updates) |
| Meteorol. forcing data (obs.-based)
 | Author | Berg et al., 2005 | GLDAS |
|  | Baseline | Re-analysis (ERA-15)  | NASA GEOS NWP analysis |
|  | Observations | Monthly | Daily/pentad |
|  | Precipitation | GPCP satellite/gauge | CMAP (5-day) |
|  | Radiation | SRB (1983-87 only) | AGRMET daily |
|  | Air temp./humid. | CRU | (None) |
|  | Horiz. resolution | ~2 deg | ~2 deg |
| In situ data |  | GSMDB | USDA SCAN |

## Satellite vs. satellite bias (time avg. soil moisture)

- SMMR retrievals **much** wetter than AMSR-E retrievals.
- Magnitude of differences comparable to dynamic range.

## We found strong biases between AMSR-E and SMMR and are working with the AMSR-E algorithm team to address the issue.

- For assimilation, we are really interested in **satellite vs. model **biases.

## Satellite vs. model bias

## Satellite vs. model bias

***Satellite vs. model bias***

##  Scale satellite data before assimilation into a model.

***Satellite vs. model bias***

**SMMR and AMSR-E exhibit ****large and *****very different***** global and regional ****biases**** in all moments relative to the model.**
**Absolute soil moisture from satellites and model agree ****equally well**** (or poorly...) with ground observations ******** no agreed climatology.**
**For seasonal forecasts, need only ****normalized anomalies****.**
****** *****Scale satellite data before assimilation into a model.***

## Soil moisture scaling for data assimilation

***Assimilate percentiles.***

## Soil moisture scaling for data assimilation (mean)

***ORIGINAL multi-year data sets***
- (Satellite minus model)

**1 year of satellite data sufficient for considerable reduction in long-term bias.**

***SCALED multi-year data sets ***
- (Satellite minus model)

***Soil moisture scaling for data assimilation (mean)***

- CDF scaling based on **1 year** of satellite data

**Reichle & Koster *****GRL *****2004**

**Reichle et al.***** JHM *****2004**

## We’re ready to assimilate!

## Global soil moisture data sets

***(upper 5...10cm and profile, point scale, hourly - 10 days)***

***Global soil moisture data sets***

- Soil moisture retrievals **not** available under dense vegetation, near open water, in frozen soil.

**USDA SCAN stations**
- ~17 of 122 useful

**More AMSR-E data than from SMMR data.**

***1. Satellite retrievals**** *
***(upper 1.25cm, 50-140km, ~3 days)***

***2. Model data***
*NASA Catchment Model (CLSM) forced w/ **observation-corrected **meteorological data.*
***(upper 2cm, ~40...150km, 6h)***

**AMSR-E (2002-05)**

**SMMR (1979-87)**

**Number of data per month**

***ASSIMILATE***

***VALIDATE***

## Validation against in situ data

**Modest increase may be close to maximum possible with *****imperfect *****in situ data. **

|  |  |  | Time series correlation coeff. 
with in situ data [-] 
(with 95% confidence interval) |  |  | Confidence levels: Improvement of assimilation over |  |
| --- | --- | --- | --- | --- | --- | --- | --- |
|  |  | N | Satellite | Model | Assim. | Satellite | Model |
| SMMR | Surface anomalies | 66 | .32±.03 | .36±.03 | .43±.03 | 99.9% | 99.9% |
| SMMR | Root zone anomalies | 33 | n/a | .32±.05 | .35±.05 | n/a | 80% |
| AMSR-E | Surface anomalies | 17 | .37±.03 | .40±.03 | .48±.03 | >99.99% | >99.99% |
| AMSR-E | Root zone anomalies | 16 | n/a | .39±.03 | .42±.03 | n/a | 91.5% |

***Validation against in situ data***

**SMMR: ****	****Reichle & Koster,***** GRL *****2005**
**AMSR-E: ****	****Reichle et al., in preparation**

## Variance of normalized innovations

**Variance deficiency in dry climates, excess variance in wetter climates.**
**Potential for improvement by (adaptively) tuning model error parameters.**

***Reichle & Koster, GRL 2005***

## Outline

| Method | Ensemble Kalman filter |
| --- | --- |
| Soil Moisture | Data, biases, and assimilation |
| Soil Temperature | Data, biases, and assimilation |

## Land surface temperature (LST) assimilation

***Problem 1*****: **
**Satellite and model LST inconsistent in vertical.**

***Good news*****: Abundance of LST retrievals from *****infrared *****and *****microwave *****sensors on *****geostationary *****and *****polar-orbiting *****platforms **
**(NOAA-xx, MODIS, GOES, METEOSAT, GMS,...)**

***Land surface temperature (LST) assimilation***

***Problem 2*****: **
**Satellite and model LST inconsistent  in horizontal.**

***Problem 3*****: Satellite LST sensor- or algorithm-specific.**

## Solution: Once again, we scale the retrievals so that they are consistent with the model states.

- 	We do *not* make use of absolute temperature values; we make use of *scaled temperature anomalies*.

## Strategy for dealing with the inconsistencies

**Scale the data prior to assimilation.**

***Strategy for dealing with the inconsistencies***

|  | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| 0z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 3z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 6z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 9z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 12z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 15z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 18z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |
| 21z | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ | μ,σ |

- 2) Scale satellite Tskin (“T_sat”) into model climatology (std normal deviates):
**T_sat_scaled = ****σ****_mod/****σ****_sat · (T_sat – ****μ****_sat) + ****μ****_mod**

- 3) Assimilate scaled satellite Tskin (“T_sat_scaled”).

## Demonstration of scaling approach

***Demonstration of scaling approach***

**	****  ****Surface meteorological forcing data:**
- 	  - Global Soil Wetness Project (GSWP-2; 1986-95)

**Model: **
- NASA Catchment land surface model on 1 degree lat-lon grid.
- (“off-line” – not coupled to atmospheric model)

**	****	****	****      ****Assimilation:**
- 			      Ensemble Kalman Filter
- 			      (developed at NASA/GMAO)

## A few days in July 1986 at Ft Peck, MT, USA

***A few days in July 1986 at Ft Peck, MT, USA***

- Tskin mean and dynamic range from satellite and model differ.  Assimilation w/o scaling increases peak Tskin.

***Tskin [K]***

## A few days in July 1986 at Ft Peck, MT, USA

***A few days in July 1986 at Ft Peck, MT, USA***

- Tskin mean and dynamic range from satellite and model differ.  Assimilation w/o scaling increases peak Tskin.
- When assimilating w/o scaling, model produces excessive sensible heat flux.
- Latent heat flux also increases when soil moisture is available.

***Sensible heat flux [W/m******2******]***

***Latent heat flux [W/m2]***

***Tskin [K]***

## “Assimilation flux”

## “Assimilation flux”

**Excessive, non-white “assimilation flux” when assimilating w/o scaling.**

## Impact on root zone moisture (Bondville, IL, USA, 1986-1995)

***Without scaling assimilation has negative impact.***

***Impact on root zone moisture (Bondville, IL, USA, 1986-1995)***

- Even though effects on monthly (incl. diurnal!) average Tskin are small,
- assimilation w/o scaling impacts latent heat,
- and eventually root zone soil moisture.

## Conclusions

**Severe mismatch between modeled land *****surface *****temperature and satellite *****skin *****temperature.**
_**Scaling needed for assimilation.**_
**Assimilation of satellite data improves soil moisture estimates.**
**Without scaling, assimilation of LST produces worse estimates.**
***Immediate future tasks:***
- Improve data assimilation:
-   - Quality control.
-   - Spatially variable model and observation error parameters.
-   - Adaptive tuning of model and observation error parameters.
-   - Implement operational land initialization for seasonal prediction (AMSR-E).
-   - Multi-variate soil moisture and temperature assimilation.
-   - Do improved land initial conditions lead to better seasonal forecasts?

***Conclusions***

## THE END.

## Monthly average assimilation flux (1986-1995)

***SUMMARY:***
***Without scaling assimilation has negative impact.***

## Global soil moisture data sets

***3. In situ data ***
***(upper 5...10cm and profile, point scale, hourly - 10 days)***

***Global soil moisture data sets***

- Soil moisture retrievals **not** available under dense vegetation, near open water, in frozen soil.

**USDA SCAN stations**
- ~17 of 122 useful

**More AMSR-E data than SMMR data.**

***1. Satellite retrievals**** *
***(upper 1.25cm, 50-140km, ~3 days)***

***2. Model data***
*NASA Catchment Model (CLSM) forced w/ **observation-corrected **meteorological data.*
***(upper 2cm, ~40...150km, 3-6h)***

**AMSR-E (2002-05)**

**SMMR (1979-87)**

**Number of data per month**

## Impact of SMMR assimilation – July 1982

**There are interesting dynamical effects.**

***Reichle & Koster, GRL 2005***

**Assimilation product lies “between” SMMR and model.**

## Anomaly time series

***Anomaly time series***

***Reichle et al., JHM,  2004, also showed that...***

## A few days in July 1986 at Ft Peck, MT, USA

***A few days in July 1986 at Ft Peck, MT, USA***

- Tskin mean and dynamic range from satellite and model differ.  Assimilation w/o scaling increases peak Tskin.
- When assimilating w/o scaling, model produces excessive sensible heat flux.

***Sensible heat flux [W/m******2******]***

***Tskin [K]***

## Satellite vs. satellite bias (time avg. soil moisture)

- Maybe the 80’s were much wetter than 2002-05...?
- Perhaps a little, but model estimates suggest much smaller differences.

***Satellite vs. satellite bias (time avg. soil moisture)***

## Satellite vs. satellite bias (time avg. soil moisture)

- Maybe the 80’s were much wetter than 2002-05...?
- Perhaps a little, but model estimates suggest much smaller differences.

***Satellite vs. satellite bias (time avg. soil moisture)***

## Soil moisture scaling for data assimilation

_**Solution:**_**  **
- Approximate CDF from many 1-year time series at grid points within some distance from point of interest.

**For “new” sensors:**
**- cannot use time series from historic satellites, **
**- long time series not immediately available!**

## Soil moisture scaling for data assimilation (std)

***ORIGINAL multi-year data sets***
- (Satellite std minus model std)

**1 year of satellite data sufficient for considerable reduction in long-term bias.**

***SCALED multi-year data sets ***
- (Satellite std minus model std)

***Soil moisture scaling for data assimilation (std)***

- CDF scaling based on **1 year** of satellite data

**Reichle & Koster *****GRL *****2004**

**Reichle et al.***** JHM *****2004**

## Validation against in situ data

**Assimilation product has improved phase of annual cycle.**

***Reichle & Koster, GRL 2005***

## Soil moisture mission planning

- Commonly, soil moisture mission planners require a measurement accuracy of ~0.04 m3/m3 (“4%”) in absolute soil moisture.
- Time-invariant errors contribute to RMSE but do not affect anomaly estimates.
- Observing System Simulation Experiment (OSSE) result:
- For a large part of the Red-Arkansas river basin, satellite retrievals might be useful (R>0.5) even though their absolute errors exceed 0.04 m3/m3.

***Crow et al., GRL 2005***

- For modeling and forecasting applications, satellite retrievals might be more useful than previously assumed.