Satellite Land Data Products and Their Assimilation into NCEP Models

X. Zhan, 12th JCSDA Workshop, College Park, MD. April 19, 2023

Satellite Land Data Products and Their Assimilation into NCEP Models

X. Zhan, J. Liu, C. Hain, L. Fang, J. YinNESDIS Center for Satellite Applications & Research, College Park, MD

W. Zheng, M. Ek, K. Mo, J. HuangNWS National Centers for Environmental Predictions, College Park, MD

L. Zhao, H. DingNESDIS Office of Satellite and Product Operations, College Park, MD

2X. Zhan, 12th JCSDA Workshop, College Park, MD. April 19, 2023

NCEP land data needs

NESDIS land satellite data availability

DA efforts at STAR with NCEP models

SMOPS and SM DA for NCEP NWP

GET-D for drought monitoring

Next steps

Outline







Next steps

Outline

NOAA Model Production Suite

Uccellini, 2009

Community NoahLand-Surface Model

Uccellini, 2009







Next steps

Outline


NESDIS Satellite Land Data Availability

Name Satellite/Sensor/System NRT Op

Sfc Type AVHRR, MODIS, VIIRS, GOES/-R Yes

Albedo AVHRR, MODIS, VIIRS, GOES/-R Yes

Fire AVHRR, MODIS, VIIRS, GOES/-R Yes

LST AVHRR, MODIS, VIIRS, GOES/-R Yes

NDVI/GVF AVHRR, MODIS, VIIRS, GOES/-R Yes

Sfc Emissivity MSPPS/MiRS Yes

SM GOES/GOES-R, GCOM-W1, SMOPS Yes

Snow AutoSnow, MSPPS/MiRS Yes

SWE AutoSnow, MSPPS/MiRS Yes







Next steps

Outline


DA Efforts@STAR with NCEP Models

Name Satellite/Sensor/System Op DA?

Sfc Type AVHRR, MODIS, VIIRS, GOES/-R Yes

Albedo AVHRR, MODIS, VIIRS, GOES/-R ?

Fire AVHRR, MODIS, VIIRS, GOES/-R Testing

LST AVHRR, MODIS, VIIRS, GOES/-R ?

NDVI/GVF AVHRR, MODIS, VIIRS, GOES/-R Tested

Sfc Emissivity MSPPS/MiRS ?

SM GOES/GOES-R, GCOM-W1, SMOPS Tested

Snow AutoSnow, MSPPS/MiRS Yes?

SWE AutoSnow, MSPPS/MiRS Yes?







Next steps

Outline


Two ways to retrieve soil moisture from satellites:

• Microwave (MW): Observed MW brightness temperature depends on soil dielectric constant that is related to soil moisture:

– Strength: higher reliability based on direct physical relationships

– Weakness: antenna technology limits spatial resolution

• Thermal Infrared (TIR): Observed surface temperature changes result from surface energy balance that is dependent on soil moisture:

– Strength: TIR sensor could have higher spatial resolution

– Weakness: relies on land surface energy balance model that is prone to input data errors.

R soil

T cT ac

H s

T s

R aH = H c + H s

R x

H c

T a

5 km

R soil

T cT ac

H s

T s

R aH = H c + H s

R x

H c

T aT a

5 km

Two-S

ourc

e M

od

el (A

LEX

I)

TRAD fc

ABL

TMI

Microwave Sensitivity By Wavelength and Vegetation Density

0.0

1.0

2.0

3.0

4.0

0 5 10 15 20 25Wavelength (cm)

Sen

siti

vity

(D

elta

TB

/ D

elta

Vol

SM

)

BARE

VEGETATION

WATER CONTENT (kg/m2)

1

2

4

0

SSM/I AMSR /WindSat SMOS / SMAP

Satellite Soil Moisture Remote Sensing Science


Soil Moisture Operational Product System (SMOPS)


Increased spatial coverage Multi retrieval variance could

be used as error estimate

Microwave Soil Moisture Products from SMOPS


Microwave Soil Moisture Products from SMOPS

WindSat SMOS ASCAT

Blended


NOAA Global Soil Moisture Data Portal:

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16X. Zhan, 12th JCSDA Workshop, College Park, MD. April 19, 2023 16


SM Data Assimilation Utility for NCEP GFS

GFSNoah

EnKF

Courtesy of R. Reichle


Assimilation of MW SM into NCEP GFS

GFSNoah

EnKF

Pros: GFS can demonstrate SM impact on forecasts GFS may take advantage of satellite SM obs earlier than

full scale implementation

Cons: Hardwiring limits more flexibility for assimilating other

observational data


Assimilation of MW SM into NCEP GFS

Time: DA at 00z

from April 1 – May 5, 2012

Data: SMOPS Blended Surface SM

Method: EnKF DA within GFS/GSI

Experiments: CTL: Regular GFS run without SM

DAEnKF: Daily EnKF run


Comparison of soil moisture from SMOPS Blended 18Z, 1-30 April 2012

GFS_EnKF EnKF-CTL

GFS_CTL SMOPS


Comparison of soil moisture from SMOPS Blended 18Z, 1-30 April 2012

SMOPS GFS_CTL

GFS_EnKF EnKF-CTL


GFS Top Lay Soil Moisture ValidationWith USDA-SCAN Measurements

1-30 of April, 2012

East CONUS (26 sites) West CONUS (25 sites) Whole CONUS

RMSE Bias Corr-Coef RMSE Bias Corr-

Coef RMSE Bias Corr-Coef

CTL 0.135 0.046 0.565 0.124 0.033 0.448 0.129 0.040 0.508

EnKF 0.130 -0.031 0.613 0.114 -0.021 0.549 0.123 -0.031 0.587

SMOPS 0.133 -0.055 0.601 0.098 -0.036 0.402 0.117 -0.048 0.524


+ 0.004

GFS 500hPa Height Anomaly Correlations


700 hPa

1000 hPa

+ 0.003

+ 0.003







Next steps

Outline


Land surface temperature (LST) and solar insolation (Rs) from NOAA Geostationary Operational Environmental Satellite (GOES) imager and future GOES-R Advance Baseline Imager (ABI) are used in an Atmosphere-Land Exchange Inversion (ALEXI) model to generate ET and an Evaporative Stress Index (ESI) for drought monitoring.

ALEXI model output using GOES data have good agreement with field observations and full-scale land surface model simulations of ET.

ALEXI ET and ESI data products are being used at US operational drought monitoring.

Thermal Infrared Remote Sensing for SM

ALEXI Drought Monitoring Webpage Used by USDA-ARS

• ALEXI Evaporative Stress Index products generated at NESDIS STAR are currently being disseminated to end-users at NDMC and CPC through a website hosted by USDA-ARS (http://hrsl.arsusda.gov/drought/index.php).

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GOES ET and Drought (GET-D) System

STAR

Input Data: LST & solar insolation from GOES imagers, Met forcing from NCEP models via NLDAS

DDS

GET-D

Linux

ESPC Production

GOES-Imager

QC/VAL

Linux

GET-D

Linux

Intranet

Sandia

Internet

gp5

CLASS

Internet

NCEP & Other USERS

ET & Drought Products

LST & Q

NCEP

Met Input

Met Input

ET & Drought Products

LST & Q

28


Palmer Drought Index

Vegetation Health Index LSM SM Output

Current US Drought Monitoring


Palmer Drought Index

Vegetation Health Index

GOES/GOES-R ESI

LSM SM Output

Satellite SM

Satellite ST, Alb, GVF/VI

Enhanced Drought Monitoring


Enhancing US Drought Monitoring with SNPP/JPPP Products

• JPSS products to be used– Surface type (ST) & soil moisture (SM)– Albedo (Al) & Vegetation Index (VI)– Land surface temperature (LST)

• Data assimilation approach– Direct insertion for JPSS ST, LST, Al and VI in NLDAS/GLDAS– EnKF for GCOM-W SM

• Drought-monitoring products– Root-zone SM anomalies– NLDAS/GLDAS runs with and without

assimilations

• Evaluation– Root-zone SM simulations vs. in situ obs– Drought monitoring products vs. standard drought indices and historical records

Proposed drought information system


Impact of Sfc Type on LSM SM simulations

In situ SM: Measurement for every 10 days from 2010-2011LSM SM: Top 10cm layer simulations

Correlation significance: black – no, blue – 0.05 credibility, green – 0.01, red – 0.001

8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 01 7

2 2

2 7

3 2

3 7

4 2

4 7

5 2 N O A H 3 . 2 & A V H R R S T

8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 01 7

2 2

2 7

3 2

3 7

4 2

4 7

5 2 N O A H 3 . 2 & M O D I S S T

8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 01 7

2 2

2 7

3 2

3 7

4 2

4 7

5 2 C L M 2 . 0 & A V H R R S T

8 0 9 0 1 0 0 1 1 0 1 2 0 1 3 01 7

2 2

2 7

3 2

3 7

4 2

4 7

5 2 C L M 2 . 0 & M O D I S S T


Impact of QC on GLDAS Soil Moisture DA

Noah LSM v3.2 was used;

In situ SM data from about 50 SCAN sites were used;

SMOPS SM: SMOS, ASCAT, WindSat

OLP: no SM DA

DA01: SM DA for all GVF

DA02: SM DA for GVF < 0.7

DA03: SM DA for GVF (ST)

Quality controlled SM data assimilation improved Noah LSM simulations


Experiments Parameters Assimilated

OLP Climatological GVF and Climatological albedo

DA01 Monthly albedo

DA02 Monthly GVF

DA03 Weekly GVF

DA04 Monthly GVF and monthly albedo

DA05 Weekly GVF and monthly albedo

Data assimilation experiment matrix

Distributions of RMSE improvement for SM simulations bet’n DA05 and OLP over 2001-2011

Average improved percentage of SM simulations Between DA01- 05 and OLP over

2001 - 2011

Impact of NRT GVF and albedo on Noah LSM SM







Next steps

Outline


GFS and LIS “Semi-Coupling”

LIS

GFS Noah

Noah

EnKF

Forcing

States

GFS Noah

LISNoah

EnKF

Forcing

States


Then, what?


Operational mode of land DA?


More questions?


Thanks!

Documents

Satellite Land Data Products and Their Assimilation into NCEP Models