The Land Information System (LIS) and Land Data Assimilation Systems (LDAS)The Land Information System (LIS) is a software framework for high-performance land-surface modeling and data assimilation. LIS was a co-winner of NASA’s Software of the Year in 2005.

A Land Data Assimilation System (LDAS) is one or more land-surface models forced with high-quality near-surface observations. Data assimilation of remotely-sensed observations are often used in an LDAS to improve modeled states and fluxes.

LIS Overview

Reference(s): Kumar et al. (2006) in Environmental Modelling & Software Peters-Lidard et al. (2007) in Innovations in Systems and Software Engineering

The Land Information System (LIS) is a flexible land-surface modeling and data assimilation framework developed with the goal of integrating satellite- and ground-based observed data products with land-surface models.

LIS Core Team: Christa Peters-Lidard, Sujay Kumar, Jim Geiger, Kristi Arsenault, David Mocko, Shugong Wang, Yuqiong Liu, Ken Harrison, Grey Nearing

LIS Investment Summary• Cost-share approach involves partner agency investments from

across the Land Surface Modeling stakeholder community: NASA GSFC, AFWA, CRREL, USDA, USAID, NOAA NCEP, NOAA ESRL, USACE, ERDC, EPA, NCAR and the academic community

• LIS project began with “seed” funding secured by NASA GSFC from HQ (Dr. Christa Peters-Lidard) via a 3-year, $1.5M NASA Earth Science Technology Office (ESTO) Computational Technologies Project (CT) CAN for “Grand Challenge Applications”– Primary advocacy provided by AFWA

Reference(s): Kumar et al. (2006) in Environmental Modelling & Software Peters-Lidard et al. (2007) in Innovations in Systems and Software Engineering

LIS Investment Summary

FY02-14 Investments to Date = $25.65M:NASA = $16.29M NOAA = $1.17M USAID = $0.67M USDA = $0.23M AFWA = $6.49M CRREL = $0.79MNASA = 63% NOAA = 5% USAID = 3% USDA = 1% AFWA = 25% CRREL = 3%

Impact of FY14 budget cut resulted in one year slip to overall AFWA 5-year (FY13-17) plan

The Land Data Toolkit (LDT) and the Land Verification Toolkit (LVT)

The current version of LIS requires a pre-processor – LDT – which processes land and parameter datasets onto the running LIS grid. LIS output can be analyzed and compared to verification/observed gridded and in situ datasets/products using the LVT post-processor.

LDT and LVT are also NASA-developed in the HSL, and released “open source” to the scientific community.

Release of LIS is subject to NASA legal review and requires a signed usage agreement.

LIS System Design

Design of LIS for NCA-LDAS

For NCA-LDAS, LIS is configured to use multi-variate (soil moisture, SCA, SWE, TWS, and irrigation intensity) data assimilation with two separate LSMs: Noah-3.3 and CLSM-F2.5. NLDAS-2 is used as surface forcing, and LVT is used to provide trends and indicators. The NCA-LDAS datasets/indicators are distributed by the GES DISC.

The Noah Land-Surface Model

The version of the Noah LSM used for NCA-LDAS is version 3.3. Noah-3.3 includes many recent model upgrades and improvements, including to warm season updates as well as snow-physics upgrades.

The community Noah LSM is maintained and released by NCAR, and is one of the LSMs available within the WRF weather forecast model. Noah is used as the land model at NCEP for weather/climate forecasts, reanalyses, and LDAS systems. The Noah LSM is also used at AFWA and by other operational systems.

Reference(s): Chen et al. (1996, JGR); Ek et al. (2003, JGR); Wei et al. (2012, HP); Livneh et al. (2010, JHM)

The CLSM Land-Surface Model

The version of the CLSM used for NCA-LDAS is version Fortuna-2.5. CLSM-F2.5 was the version used for the MERRA-Land reanalysis data product produced by the GMAO and available at the GES DISC.

The Catchment land-surface model (CLSM) is developed by the NASA Global Modeling and Assimilation Office (GMAO), and is the land-surface component of the NASA GEOS-5 GCM. CLSM divides areas into topographic catchments, which each contain a saturated fraction, an sub-saturated fraction, and a wilting fraction. These fractions evolve over time, and are used to determine fluxes and soil states within the catchment.

Reference(s): Koster et al. (2000, JGR); Reichle et al. (2011, J. Climate)

The Land Verification Toolkit (LVT)

10Reference: Kumar et al. (2012): Land surface Verification Toolkit (LVT) –A generalized framework for land surface model evaluation. Geosci. Model. Dev.

Metric Class ExamplesAccuracy RMSE, Bias, Correlation

Ensemble Mean, Standard deviation, Likelihood

Uncertainty Uncertainty importance

Information theory Entropy, Complexity

Data assimilation Mean, variance, lag correlations of innovation distributions

Spatial similarity Hausdorff distance

Scale decomposition Discrete wavelet transforms

LVT is a NASA-developed open-source software framework developed to provide an automated, consolidated environment for systematic land surface model evaluation and benchmarking

Includes support for a range of in-situ, remote-sensing, and other model and reanalysis products in their native formats

Data Integration Within a Land DataAssimilation System (LDAS)

INTERCOMPARISON and OPTIMAL MERGING of global

data fields

Satellite derived meteorological data used as land surface model FORCING

ASSIMILATION of satellite-based land surface state fields (snow, soil moisture,

surface temp, etc.)

Ground-based observations used to VALIDATE model output

SW RADIATION

MODIS SNOW COVER

PRECIPITATION

SNOW WATER EQUIVALENTExamples from NASA’s GLDAShttp://ldas.gsfc.nasa.gov/ Matt Rodell

NASA GSFC

In NCA-LDAS, satellite-based Environmental Data Records (EDRs) of soil moisture, snow cover/amount, terrestrial water storage (TWS), and irrigation intensity are used to update the model states.

March 2011 Snow Water Equiv. Mean Percentile from LPRM v5 – NASA Aqua/AMSR-E EDR (2003-2011).

March 2011 Surface SM Percentile from LPRM v5 – NASA Aqua/AMSR-E EDR (2003-2011).

March 2011 GRACE-based Groundwater Percentile from GRACE TWS EDR (2002-present).

Data assimilation in NCA-LDAS

Soil MoistureSnow, Ice, Rainfall Snow

VegetationRadiation

Aqua: MODIS, AMSR-E, etc.

GRACE

GRACE is unique in its ability to monitor water at all levels, down to the deepest aquifer

Traditional radiation-based remote sensing technologies cannot sense water below the first few centimeters of the snow-canopy-soil column

Gravity Recovery and Climate Experiment (GRACE)

The North American Land Data Assimilation System (NLDAS)

NASA/GSFC: Christa D. Peters-Lidard, David M. Mocko, Sujay V. Kumar, and members of the LIS Core team

NOAA/NCEP: Youlong Xia, Michael B. Ek, Jiarui Dong

NLDAS is a collaborative project between NOAA/NCEP, NASA/GSFC, Princeton U., U. of Washington, NOAA/OHD, et al.

NLDAS is a specific-instance of an LDAS, including the generation of a high-quality surface forcing dataset and land-surface modeling to produce consistent datasets with high spatial and temporal resolution over CONUS and parts of Canada/Mexico.

Reference(s): Xia et al. (2012a&b) – NLDAS-2 introduction and streamflow evaluation – JGR-A

NLDAS-2 used as NCA-LDAS forcing NLDAS Phase 2 extended the period of analysis to Jan 1979 to

near real-time, enabling a long-term model climatology for the development of a drought monitor

The surface forcing for NLDAS Phase 2 combines the best available observations and model reanalyses to generate precipitation, temperature, humidity, radiation, winds, etc.

The forcing is used to drive four separate land-surface models (LSMs) to provide outputs of soil moisture/temperatures, fluxes, snow cover/amount, runoff, and routed streamflow

The NLDAS-2 forcing development for the retrospective period (1979-2008) was led at NASA/GSFC, and was used to drive the GSFC Mosaic LSM using a previous version of LIS

Since 2009, NOAA/NCEP/EMC has generated NLDAS-2 forcing and run all four NLDAS-2 LSMs in near real-time (with a ~3.5-day lag)

NLDAS-2 Land Surface Forcing Hourly and on 1/8th-degree (~12.5km) resolution CONUS domain,

including adjacent parts of Canada/Mexico (25-53°N; 125-67°W) The NLDAS-2 land surface forcing dataset is a combination

of both model and observations The model-based fields are derived from the NCEP North

American Regional Reanalysis (NARR) analysis fields: NARR surface data used as base (3 hourly, 32km, Jan 1979 –

present) Elevation correction for temperature, pressure, humidity, and

longwave Includes 21 standard surface/2-m/10-m and lowest model

layer forcing fields NARR also has a real-time continuation product known as the

Regional Climate Data Assimilation System (R-CDAS), which contributes the base fields from 2003-present.

Earth Observations in NLDAS-2 The observation fields used as part of NLDAS-2 include:

NARR’s surface-based downward shortwave radiation (SWdown) is monthly bias-corrected using GOES UMD SRB SW data

Hourly NLDAS precipitation based on CPC daily PRISM-corrected gauge data, hourly Stage II Doppler radar data, half-hourly CMORPH, hourly HPD data, and 3-hourly NARR model data (depending on location and availability)

List of Earth Observations in the NLDAS-2 forcing along with coverage dates and temporal/spatial resolutions of the data:Forcing Coverage Temporal Resolution Spatial Resolution Notes

NARR Model 1979-2003 3 Hourly 32kmR-CDAS Model 2003-Present 3 Hourly 32km Realtime version of NARR

GOES Radiation 1996-2000 Hourly 1/8th degree Used to bias correct NARRCPC PRISM Gauge 1979-Present Daily 1/8th degree Used over CONUS, Mexico

CPC Gauge 1979-Present Hourly 2 X 2.5 degree Used over CONUSCMORPH Precip 2002-Present 1/2 Hourly 8km Used over CONUS, MexicoStage II Precip 1996-Present Hourly 4km Used over CONUS

LIS-based NLDAS Transitioned to Operations at NOAA/NCEPChrista Peters-Lidard, David Mocko, Sujay Kumar (NASA/GSFC/HSL/617.0)

Youlong Xia, Michael Ek, Jiarui Dong (NOAA/NCEP/EMC)

Highlight: NLDAS – the North American Land Data Assimilation System – became part of NOAA’s National Centers for Environmental Prediction (NCEP) Central Operations on 5 August 2014. It is a collaborative project between NOAA/NCEP Environmental Modeling Center, NASA/GSFC, Princeton Univ., the Univ. of Washington, NOAA’s Office of Hydrologic Development, and NOAA’s Climate Prediction Center.• NLDAS provides near real-time updates of land-surface conditions, including precipitation, temperature, soil moisture, snow, surface fluxes, and streamflow, over the continental United States. • NLDAS is also used to initialize land-surface conditions for short-term weather predictions.

ESD Applied Sciences ) Water ResourcesFigure 2: Same as Figure 1, but for for streamflow. Similar to soil moisture shown in Figure 1, streamflow in California is also significantly low.

Relevance:• NLDAS is used as an input to the U.S. Drought Monitor, which provides weekly drought assessments in the United States. These assessments are used by policymakers in allocating drought relief.• NLDAS uses the NASA-developed Land Information System (LIS) software framework. Future versions of NLDAS using LIS will include new and upgraded land-surface models as well as data assimilation of remotely-sensed land-surface states.

Figure 1: NLDAS Drought Monitor images from 24 Aug 2014 for ensemble mean soil moisture percentiles. The color bar corresponds to U.S. Drought Monitor severity categories, with darker red indicating exceptional drought. Note the severe and widespread drought in California as indicated by NLDAS.

NLDAS usage and website

http://ldas.gsfc.nasa.gov/nldas/

NLDAS is widely used for a number of different scientific studies and applications, including drought monitoring, initial conditions for numerical weather/climate simulations, watershed and water quality studies/monitoring, CUAHSI hydrologic studies, crop failure insurance estimates, West Nile virus spread and mosquito monitoring, and water management.

Distribution of all NLDAS products from the NASA GES DISC alone for calendar year 2013:• Number of Distinct users: 4,868• Number of Files: 36,148,624 (over 36 million)• Total Volume: 66,160 Gb (over 66 Tb)

NCA-LDAS Public website

http://ldas.gsfc.nasa.gov/NCA-LDAS/

Backup slides

LIS System Design

LVTLVT

LVTLVT

LVT

LDT

LIS is designed as an object-oriented framework, with abstractions defined for customization and extension to different applications. These extensible interfaces allow the incorporation of new domains, land surface models (LSMs), land surface parameters, meteorological inputs, data assimilation and optimization algorithms.

NLDAS-2 Project Land-Surface ModelsMosaic (GSFC)

VIC (Princeton Univ. & Univ. of Washington)

Noah (NCEP)

SAC (OHD)

Generation of NLDAS-2 precipitation

Dataset Years CONUS Mexico CanadaCPC daily gauge analysis

1979 – present

1/8th-degree PRISM-adjusted analysis

1/4th-degree (before 2001, 1-degree) analysis

Not used

Stage II Doppler hourly 4-km radar data

1996 – present

1st choice to temporally disaggregate

Not used Not used

CMORPH satellite-retrieved half-hourly 8-km analysis

2002 – present

2nd choice to temporally disaggregate

1st choice to temporally disaggregate

Not used

CPC HPD 2x2.5-degree hourly analysis

1979 – present

3rd choice to temporally disaggregate

2nd choice to temporally disaggregate

Not used

NARR/R-CDAS 3-hourly 32km model-simulated precipitation

1979 – present

4th choice to temporally disaggregate

3rd choice to temporally disaggregate

Used for all precip over Canada areas; a 1-degree blend near U.S.-Canada border is done.

6 Jan 2013

MODIS cloud gap filled (CGF) fractional snow cover map

Ordering Statistics: 7,417,521 MODIS snow product granules ordered in first 6 mos. of 2014 (NSIDC).

D.K. Hall & G.A. Riggs

MODIS binary snow map shows the result of a major snowstorm in the northeastern U.S.

28 Dec 2010

cloud

snow

•Products are available daily at up to 500-m resolution (2000 – present);•Dataset and algorithms were developed in the Hydrological Sciences and the Cryospheric Sciences Laboratories.

Hall & Riggs, 2007; Hall et al., 2010

MODIS snow-cover maps have been used for:•Stream-discharge modeling to support drought and flooding decisions;•Updating land-surface models, including calculating snow-water equivalent in the models;•Validating model results;•Monitoring snow-cover changes over time at regional and hemispheric scales;•Developing climate-quality data records of snow cover.

MODIS snow-cover map products