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Youlong Xia 1 , Mike Ek 1 , Eric Wood 2 , Justin Sheffield 2 ,. Lifeng Luo 2,7 , Dennis Lettenmaier 3 , Ben Livneh 3 , David Mocko 4 , Brian Cosgrove 5 , Jesse Meng 1 , Helin Wei 1 , Victor Koren 5 , John Schaake 5 , Kingtse Mo 6 , and Kenneth Mitchell 1 *.

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Youlong Xia1, Mike Ek1, Eric Wood2, Justin Sheffield2,

Lifeng Luo2,7, Dennis Lettenmaier3, Ben Livneh3, David Mocko4,

Brian Cosgrove5, Jesse Meng1, Helin Wei1,

Victor Koren5, John Schaake5, Kingtse Mo6, and Kenneth Mitchell1*

NCEP/EMC: NLDAS Support for Drought Monitoring and Seasonal Prediction

1Environmental Modeling Center, NCEP/NOAA (*retired)

2Department of Civil and Environmental Engineering, Princeton University

3Department of Civil and Environmental Engineering, University of Washington

4Hydrological Sciences Branch, NASA Goddard Space Flight Center

5Office of Hydrologic Development, National Weather service/NOAA

6Climate Prediction Center, NCEP/NOAA

7Department of Geography, Michigan State University, East Lansing, Michigan

NOAA Climate Test Bed Seminar, NASA GSFC, 14 April 2010

Collaboration Partners for Drought Monitoring

  • NCEP/EMC – Noah Model (EMC colleagues developed and executed the NARR)

    • Mike Ek, Y. Xia, Ken Mitchell* (*retired)

    • Transition of CPPA PI NLDAS research to operational prototype at NCEP

    • Noah model development and realtime run for NARR-based forcing and four models

    • Maintain and upgrade NCEP/NLDAS drought monitoring and prediction website

    • Evaluate, validate and analyze NLDAS products

  • Princeton U. – VIC Model

    • E. Wood, J. Sheffield, L. Luo* ( *now at Michigan State University)

    • VIC model development and seasonal prediction system with three methods

  • NASA/GSFC HSB – Mosaic Model

    • B. Cosgrove*, D. Mocko, C. Alonge** (*now at NWS/OHD, **now in private sector)

    • Mosaic model and retrospective land surface forcing from NARR, drought monitor web site design

  • U. Washington

    • D. Lettenmaier, Ben Livneh

    • Multi-decadal retrospective NLDAS

  • NWS Office of Hydrologic Development (OHD) – SAC model

    • P. Restrepo, J. Schaake, V. Koren

    • SAC model and precipitation analysis


    • K. Mo, H. Van den Dool, Y. Fan* (* now at NWS Office of Science and Technology)

    • User applications (especially in CPC), realtime NARR extension, US gauge precip analysis

  • NIDIS Drought Monitor Group ( – Eric Lubehusen, USDA and

    application to US Drought Monitor

NLDAS Drought Monitoring ConfigurationN. American Land Data Assimilation System

  • Uncoupled land model simulations

    • four land models: Noah, VIC, Mosaic, SAC

  • CONUS domain

    • 1/8th degree resolution (daily gauge precipitation)

  • Common land surface forcing

    • hourly and 1/8th degree

    • Jan 1979 to present realtime

  • Retrospective mode

    • 30-year: 1979-2008

    • 15-year spin-up

    • 30-year climatology for each land model (1979-2008)

Soil-Vegetation-Atmosphere-Transfer Scheme Community



NCEP operational land model

Hydrology Community

NWS operational hydrologic model



SAC Model

NCEP/NLDAS Drought Monitoring and Prediction Website


Drought Prediction


Drought Monitor

Anomaly and percentile for six variables and three time scales

The web shows not only four-model ensemble mean anomaly and percentile but also separate model anomaly and percentile.

GSWP2 results demonstrated that multi-model ensemble mean gives more robust simulations when compared to the separate model simulations. Therefore, ensemble mean results will be shown in NLDAS drought monitoring

An Example

June 1998 – drought year

Monthly total column soil moisture anomalies

and model spread (mm/month)

Large similarity and small spread

July 1993 – flood year

Monthly total column soil moisture anomalies

and model spread (mm/month)

Similar characteristics and large spread

Direct application of NLDAS products to USDM

NLDAS soil moisture and total runoff products are provided to Eric Lubehusen at USDA who is one of the authors of the US drought Monitor. He created top 1m and total column soil moisture images and sent them to the entire Drought Monitor group (contour – US Drought Monitor boundary, and shaded plot is NCEP NLDAS ensemble mean percentile).

NLDAS Forcing and Model Products to directly Support NCEP/CPC Monthly Drought Briefing

Extension from 1/8o (14 km) to 4 km (HRAP grid) resolution

(Hydrologic Rainfall Analysis Project)

NWS/OHD SAC Total Soil Moisture Anomaly

Current NLDAS

High Resolution NLDAS

July Climate

July 1988

July 1993

Validation and assessment initiative

of NLDAS products using in-situ observations and satellite retrieved data

Validated Products:

  • Forcing – solar radiation, downward long-wave radiation, air temperature, precipitation, relative humidity

  • Streamflow and evaporation over the US

  • Soil moisture

  • Soil temperature

  • Snow water equivalent and snow cover

  • Skin temperature

  • Surface energy fluxes – sensible heat, latent heat, ground heat , net radiation

Inter-comparison of model products

  • Similarities of different model products

  • Comparison of phase 1 and 2 for the same period

  • Comparative analysis of relationships between forcing (P, T) and water and energy fluxes

Simulation skills: Correlation between observed and simulated monthly soil moisture anomalies averaged over 16 sites of Illinois for four land surface models (1985-2004, white color shows the correlation is not significant at 95% confidence level)

weak correlation

strong correlation

A validation example

Distribution of NLDAS Products

31 years (1979-2009), hourly temporal resolution, 1/8th degree spatial resolution, NLDAS region.

Forcing data and outputs from four land models

Future Work

- Transition NLDAS to NCEP operation

- Evaluation and validation of NLDAS products

- Extend current NLDAS system to Land Information System

(LIS) and to use ensemble Kalman filter to assimilate obsrvations

- Improvement of land surface models

through collaboration with NLDAS partners

Drought Prediction over the Continental US

Using the Seasonal Forecast System

Developed by Princeton University and

University of Washington

Collaboration Partners

for NLDAS Seasonal Drought Prediction

NCEP Environmental Modeling Center (EMC)

Mike Ek, Youlong Xia, and Ken Mitchell (retired)

Transition seasonal hydrological forecast system to EMC as a test system

Depending on initial conditions of VIC water mode being run by Lifeng Luo

Princeton University

Eric Wood, Lifeng Luo, Justin Sheffield, and Haibin Li

Developing seasonal hydrological forecast system using NCEP Climate Forecast System (CFS) forecast products and Ensemble Streamflow Prediction (ESP) approach

Assessment of seasonal forecast system using CFS reforecast products (not funded yet)

Michigan State University

Lifeng Luo (moved from Princeton University)

Extension of seasonal hydrological forecast system to SAC and Noah (not funded yet)

Run nowcasting of VIC water mode

University of Washington

Dennis Lettenmaier, Andy Wood, Ben Levnieh

Developing seasonal hydrological forecast system using NCEP Climate Prediction Center (CPC) seasonal outlook products

NCEP Climate Prediction Center (CPC)

Kingtse Mo and Jin-Ho Yoon

Test various bias correction schemes to improve seasonal hydrological

forecast system skills at Southeast and Colorado basins

Current Forecast System

(Foundation Work)


Bayesian Merging of Information


Likelihood function

(relates local scale to GCM scale and above)


(local climatology)

1/8th degree

scale variable

Variable at GCM scale

and above

Bayes Theorem

Flowchart of Seasonal Hydrologic Ensemble Prediction System

GCM Seasonal Forecast

(ensemble forecasts)

GCM resolution

monthly time step

Bayesian Merging

Downscaling (Ta, P)

Meteorology climatology

1/8 degree

daily time step

“Weather” Generator


Hydrology climatology



Land surface

(hydrology) models





Ohio Basin Verification

Performance of different approach

West and East US Drought 2007




UW: CPC Approach



From outlook forecast products to get model forcing

NLDAS Forecast Products

6 month forecast using three approaches [CFS, CPC, and Ensemble Streamflow Prediction (ESP)]

Monthly mean total soil moisture anomaly and percentile, evaporation anomaly and percentile, streamflow anomaly and percentile, precipitation anomaly, drought probability forecast

CFS Forecast Products – an example

2-month forecast precipitation anomaly

Forecast total column soil moisture anomaly

Forecast total column soil moisture percentile

Forecast evaporation anomaly

Forecast evaporation percentile

Forecast baseflow anomaly

Forecast baseflow percentile

Forecast soil moisture drought probability

Streamflow Forecast

Similar variation tendency

There are differences for different approaches

Need to be evaluated using USGS gauge

staremflow measurement


Support NCEP/CPC Seasonal Drought Outlook

CFS 1-month forecast

Drought will continue for a while

CFS 2-month Forecast

CFS 3-month Forecast


Future Operational Version

Multi-models and multi-methods forecast system

From Current System to Future Operational Version

Much research needs to be done

before operational transition!

  • To extend current system to Noah, SAC, and Mosaic (CLM) to achieve multiple models

2. Assessment of the system using 30-year (79-08) CFS reforecast products

3. To use multiple coupled GCM models forecast products (through climate testbed) to improve model forcing reliability

4. To add short-middle term forecast products to enhance forecast skills

To support NIDIS

To support NCEP/CPC seasonal drought outlook and drought prediction of National Integrated drought Information System (NIDIS), a stable and reliable operational multi-models and multi-methods seasonal hydrological ensemble forecast system is required.

However, this system heavily depends on more research from our NLDAS collaborators and US research community to make this system mature so that it can be transitioned to NCEP operations.

North American Land Data

Assimilation System (NLDAS) Phase 2

Thank You

Welcome to use our products!

Thank you!

Suggestions and comments to:

[email protected]

[email protected]

Eric Wood:[email protected]

Lifeng Luo:[email protected]

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