# Presentation: 041574

## Satellite-derived fields for Estimating Soil Moisture for Regional WRF Model Initialization

**John R. Mecikalski**1
- _Collaborators_
**Martha C. Anderson**2**, Christopher Hain**1**, John M. Norman**2
- 1*Atmospheric Science Department*
*University of Alabama in Huntsville*
- 2*University of Wisconsin–Madison*
*Soil Science Department*

**Supported by:**
**The NASA SPoRT Initiative**

## O U T L I N E

*Turbulent Fluxes, Available Soil Water*
- ALEXI Soil Moisture Validation
*Comparison to regional soil moisture measurements*
- WRF Model Initialization with ALEXI (versus EDAS)
*Progress toward routine usage*

- _O U T L I N E_

***Satellite-derived fields for Estimating Soil Moisture for Regional NWP Model Initialization***

## Overview: UAH Contributions

**Diagnostics:** ALEXI land-surface (*S*o, *A**w*, *R*net, *ET*) fields, ADAS surface *T*a. All at 2-10 km resolution.
**Nowcasting (0-6 h):** Convective initiation (CI), Lightning Initiation & First Lightning; “*CI Index*” for 2-6 h CI (based on satellite & NWP model fields). Aviation Safety (ASAP).
**Short-term Prediction (6-24 h):** Utilize “diagnostics” as satellite-based boundary conditions, ADAS populated by remote sensing data (satellite & radar) toward a high-resolution (5-10 km) regional initialization for ARPS, WRF, etc.
**UAH Graduate students:** NWS SCEP, MS/Ph.D. studies that involve NWS interactions. Developing “*in-house*” nowcasting expertise.

**An ability to Leverage from other NASA, NSF, etc. projects,**
**	****to the SPoRT Center**

## Overview: UAH Contributions

**Diagnostics:** ALEXI land-surface (*S*o, *A**w*, *R*net, *ET*) fields, ADAS surface *T*a. All at 2-10 km resolution.
**Nowcasting (0-6 h):** Convective initiation (CI), Lightning Initiation & First Lightning; “*CI Index*” for 2-6 h CI (based on satellite & NWP model fields). Aviation Safety (ASAP).
**Short-term Prediction (6-24 h):** Utilize “diagnostics” as satellite-based boundary conditions, ADAS populated by remote sensing data (satellite & radar) toward a high-resolution (5-10 km) regional initialization for ARPS, WRF, etc.
**UAH Graduate students:** NWS SCEP, MS/Ph.D. studies that involve NWS interactions. Developing “*in-house*” nowcasting expertise.

## SWdown

**SW**down

**SW**up

**LW**down

**turbulent**
**exchange**

**latent**
**heat**

**SW**up** & LW**up

**“****Simplified” Surface Energy Budget:**
**	****Remotely Sensed Components**

**GOES**
**AVHRR/MODIS**
**LandSat-ETM+**

**LW**up

## Rnet = H + LE + G  (Retrieval/Remote Sensing) 

*R**net** = H + LE + G  *(Retrieval/Remote Sensing)* *
*	**R**net** = SW*down* + LW*up/down*  *(Remote Sensing)* *

**Surface Energy Balance Equation**

**Components Measured through Remote Sensing:**

**Shortwave Down** (*SW*down, *S*o): Diak et al. (1996)
**Surface Turbulent Fluxes **(*H*, *LE*)** and Soil Flux **(*G*): ALEXI

##  Solar (SWdown/So):

**  **One of the (under-utilized) success stories of satellite meteorology
-   Many “snapshots” (hourly) results from geostationary platforms, time-integrated
-   Simple atmospheric physical model with measured surface albedo used for
-    cloud detection, quantifying cloud albedo, radiative transfer effects in clear and
-    cloudy atmospheres
-  Several methods using GOES/Meteosat/GMS data by independent investigators
-  Daily* S*o usually with < 10% error versus pyranometers
-  20 km resolution *S*o Product: North and South America, Australia, Europe

**Component Methodologies**

## Physical Model

## Solar Insolation Validation & Products

- Otkin’s stuff

**Hourly GOES**
**Insolation**
- versus
**Surface**
**Observations**

## Solar Insolation: ALEXI Input

- 2 km

## ALEXI Characteristics

**• **Takes into account angular dependence of Tb on view angle using “two-source” model
- • Uses time-difference, reduce bias other errors PBL closure (instead of measurements at anemometer height) reduce sensitivity to BC
- • Computes Ta from PBL
- 	closure rather than
- 	requiring a measurement
- • Linked to MM5 forecast
- 	model for required input
- 	meteorology
- Nearly 10-years of
- 	development

**ALEXI Characteristics**

## Slide 12

## Surface turbulent fluxes (H, LE) and soil flux (G)

*Primary satellite inputs*:   	1) Time Change Radiometric Temperatures (GOES)
- 			2) Fraction Cover from NDVI (AVHRR, MODIS)

## MODIS Data Usage within ALEXI

- Employ disaggregation using ALEXI (“DisALEXI”) with MODIS thermal data as twice-daily input.
- MODIS 250 m visible to sharpen thermal data to >1 km resolution.
- Develop regional-scale (e.g., over continental U.S.) disaggregation procedures that relies on MODIS imagery when available.
- Develop field-scale available water data sets at MODIS resolution for agriculture and NWP applications.
- Use MODIS land-surface products (e.g., NDVI, LAI).

**1 km resolution**

**250 m resolution MODIS**

**MODIS direct broadcast capabilities at UW**

- Missouri

- Arkansas

- Illinois

##  MODIS Land-Cover Data:

**  **Transition from AVHRR NDVI & fraction vegetation cover to MODIS (“**MOD15A**”
- Collection 4)
-   Anderson et al. (2006); Dr. M. C. Anderson (with NASA funds) gets credit for this
- development

**ALEXI Components: Driven by MODIS**

- MODIS vs. LandSat

## Examples of Satellite Inputs for ALEXI

**Examples of Satellite Inputs for ALEXI**

## ALEXI Daily Fluxes & Flux Climatologies

- 	satellite-estimated radiation streams from GOES & AVHRR.

**•** “*Available Water*” computed using fluxes for soil and vegetation
- 	when clear.  Carried through when cloudy using satellite radiation
- 	estimates to maintain continuous daily flux budgets. For NWP data
- 	initialization.

**ALEXI Daily Fluxes & Flux Climatologies**

**•** In the process of developing a 4-year flux climatology over seasons,
- 	months, and over various regions of the U.S.

**•** High resolution fields (250 m–1 km) soil conditions, ET, etc. for
- 	agriculture.

## ALEXI Model: Daily Regional Flux Mapping

- _Region Fluxes _(Wm-2):
- 	5 km Resolution

- _United States Fluxes _(Wm-2):
- 	10 km Resolution

## ALEXI Model: Daily Regional Flux Mapping

- _Instantaneous Fluxes _(Wm-2):
- 	At Local Noon-1.5 h

- _Daily Average Fluxes _(MJm-2):
- 	Clear & Cloudy Regions

## O U T L I N E

*Turbulent Fluxes, Available Soil Water*
- ALEXI Soil Moisture Validation
*Comparison to regional soil moisture measurements*
- WRF Model Initialization with ALEXI (versus EDAS)
*Progress toward routine usage*

- _O U T L I N E_

## ALEXI Derived Soil “Root Zone” Available Water

**a)** 6-day composite of system (soil+canopy) potential ET fraction estimates from the ALEXI model, ending 1 July 2002.
**b)** 6-day Accumulated Precipitation
**c) **Canopy Potential ET fraction (*Root Zone Available Water*).
**d)** Soil Potential ET fraction with lowest stress in red (*Surface Layer Available Water**)*

- a)

- c)

- d)

- b)

**Low**
**Stress**

**SMEX**

**High**
**Stress**

**Anderson et al. (2003)**

- High                   Low

- STRESS

- Low                  High

- PRECIPITATION

- (5 km resolution)

## An Example over the Oklahoma

**July 15, 2003 Radiometric ****∆****T**

**July 15, 2003 Vegetative Cover**

**July 15, 2003 Total System ET**

**An Example over the Oklahoma**
**Mesonet:**
**Chris Hain ****(UAH)**

## Verification of soil moisture products can be very difficult :

**Verification of soil moisture products can be very difficult :**
-  Lack of large observational soil moisture networks
-  Problems verifying a point observation with a 10km x 10km pixel (ALEXI)
-  Have chosen to verify ALEXI through time series correlations using spatially averaged 	soil moisture observations from the OK Mesonet and SWATS soil moisture 	`	observations.

##  Assume that ALEXI’s total system evapotranspiration is an integrated average of available water within the 0-200 cm soil column. Unfortunately, we know very little about the distribution of available water within that 0-200 cm soil column.

-  Assume that ALEXI’s total system evapotranspiration is an integrated average of available water within the *0-200 cm soil column*. Unfortunately, we know very little about the distribution of available water within that 0-200 cm soil column.
-  Using the available water profile from NAM/EDAS initialization fields as a first guess of the soil profile, and adjust this profile to fit the integrated average of available water from ALEXI.

**ALEXI Volumetric Soil Moisture (0 – 10 cm) – Sept 29, 2003**

## Highest

- The NOAH-LSM uses a 4-layer soil moisture model to handle the exchange of soil water between the surface, sub-surface and atmosphere.
- Under high vegetative cover, ALEXI loses some sensitivity to handle the surface layer (0-10 cm), and under low vegetative cover, ALEXI loses some sensitivity to the sub-surface layers (10-200 cm).
- We assume that a fraction of evapotranspiration is directly (1:1) related to a fraction of available water, which in turn can be used to calculate a volumetric soil moisture, given values of field capacity and permanent wilting point for the soil type.

- 0 – 10 cm

- 10 – 40 cm

- 40 - 100 cm

- 100 – 200 cm

- impact
- zone
- for
- improvement via **profile**
**adjustment**

- rainfall influenced

## Largest

**ALEXI Total AW (14-day average)**

**NAM/EDAS Total AW**

**14-day Precipitation ending June 2**

**Soil Moisture Initialization**
- 2 June 2002
- WRF Model Experiments
**Chris Hain** (UAH)

- Differences

## Data Assimilation plans for 2006

- routine (daily) assimilation within SPoRT WRF (in conjunction with MODIS SSTs).
**Theme 2:** Work ALEXI into an integrated Soil Moisture assimilation scheme that takes
- advantage of Microwave moisture estimates.
**Theme 3:** Begin the assimilation of ARMOR radar product fields (with Walt Petersen).
**Theme 4:** Routine ADAS analyses for NWS (“*surface-only*” and “*regional-3D*”)
**Theme 5:** MIPS-based “sensitivity-driven” (via Ensemble Kalman Filter) assimilation tool.

***Data Assimilation plans for 2006***

## Contact Information/Publications

***Contact Info:***
- Prof. John Mecikalski: *johnm@nsstc.uah.edu*
- Chris Hain: *hain@ssec.wisc.edu*
- Martha C. Anderson: USDA (see me for email)
***Web Page:***
- nsstc.uah.edu/johnm/alexi/
***Publications:***
- Hain, C., and J. Mecikalski, 2006a: ALEXI soil moisture validation... Conf. Satellite Meteorology and Oceanography. Atlanta, GA
- Hain, C., and J. Mecikalski, 2006b: WRF-model initialization with ALEXI available soil moisture estimations... Conf. Satellite Meteorology and Oceanography. Atlanta, GA
- Hain et al., 2006/07: Formal publication. In preparation. *J. Hydrometeor.*