# Presentation: 118082

## The Multi-Institution North American

**Land Data Assimilation System Project: **
**                       ****(N-LDAS)**

**Ken Mitchell**
**NCEP Environmental Modeling Center**

***Mississippi River Climate & Hydrology Conference***
***15 May  2002***

**P. Houser, E. Wood., A. Robock, J. Schaake, D. Lettenmaier, **
**D. Lohmann, B. Cosgrove, J. Sheffield, L. Luo, Q. Duan,**
**W. Higgins, R. Pinker , D. Tarpley, J. Meng**

## Improving Weather and Climate Prediction:

**BecominIg a Complete **_**Earth System**_** Endeavor**

- 1 - _ATMOSPHERE_:	troposphere, stratosphere
- 			*- initial conditions require *
*	**	**	**  *_*atmosphere data assimilation*_
- 2 - _OCEAN_:		deep ocean, seas, coastal ocean, sea ice
- 			*- initial conditions require*
*	**	**	**  *_*ocean data assimilation*_
- 3 - _**LAND**_:		soil, snowpack, vegetation, runoff
- 			***- initial conditions require***
***	******	******	******  ***_***land data assimilation***_

## LAND DATA ASSIMILATION SYSTEMS:

**•**** ****Modern NWP &  ****Seasonal Forecast Climate models**** must model and initialize  the entire "Earth System“**
- _Atmosphere_
- _Ocean_
_**Land**_**   ********   (Land Data Assimilation Systems: LDAS)**
- •   **Three Broad Approaches to Land Data Assimilation**
**1) **_Coupled __Land/Atmosphere 4DDA_ e.g. Global Reanalysis-I
- precipitation forcing at land surface is from parent atmospheric model
- surface insolation at land surface is from parent atmospheric model
- precipitation/insolation may have large bias: >large soil moisture bias
**2)** _Uncoupled__ Land 4DDA (land model only)_ e.g. N-LDAS
- observed precipitation/insolation used directly in land surface forcing
**3)** _Hybrid__ Land 4DDA_  e.g. Eta/EDAS, Global Reanalysis-II
- Coupled land/atmosphere, but observed precipitation replaces model precipitation for driving the land surface

## N-LDAS Design(The Uncoupled Approach)

**1. Force models with 4DDA surface meteorology (Eta/EDAS), except **_**use actual observed precipitation**_** (gage-only daily precip analysis disaggregated to hourly by radar product) **_**and hourly downward solar**_** insolation**** (derived from GOES satellites).**
**2. Use 4 different land surface models:**
-   NOAH (NOAA/NWS/NCEP)
-   MOSAIC (NASA/GSFC)
-   VIC (Princeton U./ U. Washington)
-   Sacramento (NOAA/OHD)
**3.  Evaluate results with all available observations, including soil moisture, soil temperature, surface fluxes, satellite skin temperature, snow cover and runoff.**

## N-LDAS Objectives:

**1)**_**Provide land-state initial conditions**_** (soil moisture, snowpack) for:**
**	****a) realtime coupled model forecasts of weather / seasonal climate**
**	****b) retrospective land-memory predictability studies**
**2) **_**Improve LSM physics**_** by sharing methodologies / data sources**
**3) **_**Identify causes of the spread**_** in magnitudes of soil moisture, surface water/energy fluxes, and surface water storage typically seen in LSM intercomparisons**
**4) **_**Compare land states**_** of the **_**uncoupled**_** LDAS with traditional or hybrid **_**coupled**_** land/atmosphere 4DDA**
**      ****-- e.g. Global and Regional Reanalysis**
**5) ****Pending:****Demonstrate how to **_**assimilate**_** land-state related **_**satellite**_** retrievals (e.g., snowpack, skin temperature, soil moisture)**

## N-LDAS Objectives: Cont’.

**Determine if macroscale, physically-based, distributed SVAT-type land models can achieve skill in streamflow simulation commensurate with traditional lumped catchment models (both calibrated and uncalibrated, both research and operational).**

- A shared objective with the DMIP Project of NWS/OHD,
- but on larger scale at coarser resolution.

## The GAPP/GCIP Vision:

**hydrological prediction by ****bringing together**
_**meteorologists**_** and **_**hydrologists **_**in:**
** ****coupled land-atmosphere modeling**
**(B) ****uncoupled land modeling and land data 4DDA**
**(C) water resource applications of ensemble weather**
**       ****and climate forecasts**

**The GAPP/GCIP Vision:**

## Operational

- Figure 2.  The GCIP implementation framework.

***DATABASE***
***DEVELOPMENT***

***ROUTINE***

***ENHANCED***

***EOP***

***RETROSPECTIVE***

***Standard Resolution***
***Meteorological***

***Nonstandard Resolution***
***Meteorological***

- Precipitation
- Radiation and Clouds
- Wind
- Water Vapor

***Hydrological***

- Runoff
- Soil Moisture
- Evaporation
- Snow (water equivalent)

***Geographical***

***Vegetation***

***Field Campaign(s)***

***OBSERVATIONS***

***MODEL DEVELOPMENT***

***Operational***
***Path***

***Research***
***Path***

***Evaluate Current***
***  ******Models (GCM and Meso)***

***Implement Upgraded***
***  ******Land Surface Hydrology***

***Improve Precipitation and***
***Cloud   Parameterizations***

***Algorithm Development***
***Data Analysis***
***Data Assimilation***

***Intercompare***
***  ******Surface Hydro-***
***  ******logical Models***

***Scale Analysis***

***Improve Atmos-***
***  ******pheric Models***

***Develop New***
***  ******Macroscale***
***  ******Hydrologic***
***  ******Models***

***Evaluate and***
***  ******Improve***
***  ******Coupled Models***

## N-LDAS PROJECT GOAL:

**Carry the distributed macro-scale legacy of the **
**PILPS-2c and GSWP projects of GEWEX into**
**a **_**realtime demonstration realm**_**, in particular at NCEP for use in NCEP seasonal climate and weather prediction suites.**

## N-LDAS PROJECT GOAL:

- _Bring together_ multi-institution (operational center, government labs, universities) GAPP-funded investigators (both research and operational), and their
- land models
- land-relevant satellite observations, products, databases
- land in situ observations, products, databases
- land validation tools and techniques, and
- land models and modeling expertise,
- land stakeholder applications,
-      into a joint, common, cohesive and ongoing _**realtime demonstration project**_, with clear _**follow-on operational potential**_, and highlighted by frequent and open infusion and sharing of methodologies, ideas, insights, and experiences.

## N-LDAS Collaborators

**NOAA                 NASA               Universities*****                            ******	******               http://ldas.gsfc.nasa.gov***

## GAPP/GCIP Land Models, Products, Observations, and Validation Tools brought together in the N-LDAS Project:

**Mitchell/Lohmann:**** **NOAH, realtime forcing & LSM output, streamflow
**Houser/Cosgrove:** MOSAIC, retrospective forcing, LDAS web site
**Wood/Lettenmaier: **VIC, stream connectivity, snow validation
**Schaake/Duan: **SAC, precipitation analysis, soil properties
**Robock/Luo: **validation of soil moisture, forcing, fluxes
**Higgins et al: **precipitation forcing
**Tarpley: **realtime satellite solar insolation, skin temp, snow cover
**Pinker et al: **retrospective satellite solar insolation, skin temp
**Crawford et al: **OU Mesonet forcing and soil moisture/temp
**ARM/CART: **surface forcing, surface fluxes
**Augustine/Meyers: **SURFRAD surface-based solar insolation obs
**Lakshmi/Syed: **geo-statistical assessment of N-LDAS states/fluxes

## LDAS Implementation

**LDAS Implementation**

**LSM Models**:  MOSAIC, VIC, NOAH, Sacramento
- 1/8-degree resolution, hourly output
- Runoff routing: calibration, validation
**Surface Characteristics**:
_*Vegetation*_: UMD, EROS IGBP, NESDIS greenness, EOS products
_*Soils*_: STATSGO, IGBP; _*Terrain / Land-Mask*_: 1-km digital elevation

_**Soil type**_** on LDAS grid**

_**LDAS predominant vegetation**_** from 1km EROS data**

## LDAS Implementation (cont.)

**Forcing:** (top two are non-model based)
- _Precipitation_: Higgins et al daily gage anal, NCEP/OH Stage IV gage/radar
- _Radiation_: NESDIS 0.5-degree hourly GOES solar insolation
- _Meteorology_: NCEP EDAS (Eta 4DDA) analysis (wind, temperature,
-                       pressure, humidity, downward longwave)

**GOES shortwave radiation [W/m^2] 20011101 18Z**

## LDAS Run Modes:1) Realtime, 2) Retrospective

**-- **_**NCEP**_** realtime forcing **
**RETROSPECTIVE****: ****01 Oct 1996 to 30 Sep 99**
**-- Mandated largely by spin-up issues**
**-- **_**NASA**_**-assembled retrospective forcing**
**--- **Higgins NCEP/CPC reprocessed precipitation forcing:
- 	**---- **more gages obs, more QC
**--- **Pinker U.Md reprocessed solar insolation forcing
-       **---- **better cloud screening, more QC
_**Rutgers University**_** compared the soil moisture, soil temperature, surface flux results from the retrospective LDAS runs to observations over Oklahoma/Kansas for last retro year.**

**LDAS Run Modes:****1) Realtime, 2) Retrospective**

## LDAS Soil Wetness Comparison

-      LDAS retrospective output example
-      (similar spread as in PILPS-2c)

## LDAS Soil Wetness Comparison

- _LDAS Soil Wetness Comparison_

-      LDAS realtime output example
-      (similar spread as in PILPS-2c)

## LDAS Forcing Validation 2001 08-11

**Monthly mean diurnal**
**solar insolation intercomparison**
**GOES**
**EDAS**
** ****AGRMET**
**vs**
**SURFRAD**
**SURFRAD**

## LDAS-NOAH Skin Temperature 

- LDAS-NOAH Skin Temperature
- October 2001 Validation cont.

- Region 2

- Region 5

- 15 Z

- 21 Z

## Shallow/retreating snow cover in USA northern plain states

- Snow depth from USAF, cover: global 1/8 bedient, unit [in], daily
- Snow cover product from NESDIS daily, cover: 1/16 bedient N.Hemisphere grid, flag

- Snowpack Simulation Comparison

-  = estimated

-  = future

## Snow Cover Validation

**Comparison of Model Snow Cover with IMS (observed) **

- RFC Regions

- % of Region Covered with Snow

## LDAS Models Streamflow

- 02192000 = Broad River, GA, 1430 sq. miles
- 01631000 = Shenandoah River, VA, 1642 sq. miles
- 01503000 = Susquehanna River, NY, 2232 sq. miles

## LDAS Scientific Questions

**1.****	****Can land surface models forced with observed meteorology and radiation reproduce point-wise           soil moisture/temperature states and surface fluxes?**
**If not, what are the relative contributions to the differences between models and observations owing to   ****a) errors in the soil-state/surface-flux observations****    or     ****b) differences in the following between model and observed****:**
**	****a.  Forcing?**
**	****b.  Soil properties?**
**	****c.  Vegetation characteristics?**
**	****d.  Scales of representativeness?**
**	****e.  Vertical resolution?**
**	****f.  Other (e.g. tiling, variable infiltration assumptions)**

## LDAS Evaluation Issues

** ****Vegetation state**
** ****Vertical resolution**
** ****Soil type**
** ****Precipitation**
** ****Radiation**
** ****Spatial and temporal scales of soil moisture variations**
** ****Averaging soil moisture from a mosaic tiling approach**
** ****Interpreting soil moisture from variable infiltration approach**

**Differences between observations and models**

**Differences in forcing between  local observations and gridded forcing for models**

## Soil Moisture/Temperature Observations

## Oklahoma Mesonet

- 115 Mesonet stations covering every county of the state
- Meteorological observations are taken at 5 min intervals:
- Relative Humidity at 1.5 m
- Air Temperature at 1.5 m
- Average Wind at 10 m
- Precipitation
- Station Pressure
- Solar Radiation
- 72 stations have soil moisture and soil temperature observations taken at 15 min intervals.

## LDAS Radiation Validation: Shortwave / Longwave(Gridded 1/8-th degree vs Pointwise Station)

- Jan 98 – Sep 99

- _Shortwave_

- _Longwave_

## VIC Simulation with Soil Type Matching Local Type(at clay-loam site ALTU)

## VIC Simulation with Unmatched Local Soil Type(at sand site MANG)(Note: observed soil moisture somewhat suspect at all sand sites)

## Soil Moisture Anomaly Validation

## Geo-Statistical Analysis of 

**Geo-Statistical Analysis of **
**LDAS Forcing, States, Fluxes**