# Presentation: 554324

## NASA and NOAA Surface Water and Energy Balance Data for  Water Resources Applications

**NASA and NOAA Surface Water and Energy Balance Data for  Water Resources Applications**
** ****David Toll (NASA/GSFC****)*,**** Jiarui Dong (UMBC/GEST), Paul Houser (GMU/CREW), Kristi Arsenault (UMBC/GEST), &  Brian Cosgrove (NASA/SAIC)**
**********NASA/GSFC Hydrological Sciences Branch/614.3, Greenbelt, MD 20771 USA   ***_[{David.L.Toll@Nasa.gov](mailto:%7BDavid.L.Toll@Nasa.gov)__, 301-614-5801}_

**Abstract**

**The primary objective of the study is to evaluate surface water and energy balance for water resources applications through study of the ****North American Regional Reanalysis (NARR) and the North American Land Data Assimilation System (NLDAS) systems.  For reference we used *****in situ***** data from the Coordinated Enhanced Observing Periods (CEOP 3&4),  the Oklahoma Mesonet sites, and test sites in the Middle Rio Grande.  In this paper we emphasize results from the CEOP reference site.  The high spatial resolution (to 1km and finer) NASA Land Information System (LIS) was used to further study factors affecting modeled surface water and energy budget data and also towards more accurate water fluxes for higher resolution water resources applications. ****Overall the NARR and NLDAS atmospheric forcing compared well with the CEOP *****in situ***** data.   However, a****nalysis of results for the study sites showed significant biases for the partitioning between sensible heat and latent heat fluxes during spring and summer seasons.  We used LIS to further investigate the use of improved: 1) atmospheric model forcing; 2)  land surface model (LSM) physics; and 3) model input parameters from satellite data.**

**  **

**Assessment of the Atmospheric Forcing **

**Acknowledgements**

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**FORCING DATA**

**This work was funded under the NOAA-CPPA.  Additional support was provided by the NASA-HQ Terrestrial Hydrology Program.**

**Figure** Land Information System (LIS) is a high performance land surface modeling and data assimilation system capable of modeling global land-atmosphere interactions at spatial resolutions to 1km and finer.  LIS was used to further investigate factors affecting surface water and energy budget data. {http://lis.gsfc.nasa.gov}

**Using ‘Land Information System’ to Evaluate Surface Heat Fluxes**

-            Summary

**This study evaluated the NOAA and NASA land surface water and energy balance data oriented towards weather climate studies having coarser resolution (>12km) with a primary goal towards extending these systems to finer resolution (<1km) useful for water resources applications.   When comparing the NASA and NOAA atmospheric forcing data to CEOP in situ data we generally found a very good correspondence.  For the summer months we found a sensible versus latent heat energy partitioning problem. **** ****We used the NASA high resolution Land Information System (LIS) to further assess and develop 1) improved atmospheric forcing, 2) land surface model physics, and 3) satellite updated surface albedo and land cover. **

**CEOP3&4.  The Third & Fourth Coordinated Enhanced Observing Periods****.**
- 	-GEWEX Americas Prediction Project - Atmospheric Radiation Measurement (GAPP-ARM) Southern Great Plains (SGP) sites.
- 	-  October 2002 to December 2004. 1⁄2 Hr Intervals
- 	- 12 sites with near-complete surface heat fluxes, 7 sites also include full meteorological forcing data.** **

**We constructed LIS runs for the study area at a 1 km spatial resolution with three land surface models (Noah, Mosaic & NCAR CLM2) to further examine ways to improve surface water and energy flux accuracy data at finer resolutions needed for water resources applications.**

**Replaced “NLDAS Forcing” with “In Situ Forcing”**
- -  Shortwave, Precipitation, Longwave (), Ta, Qa & P
- Most significant change from Shortwave Radiation.
- Improved surface heat fluxes for all 3-models (SH+LH shown).
- Large SW Errors, April-August 2003 (RMS of 220 vs. 50 W/m2).  NASA/GSFC Mosaic different from NCEP Noah.

** ****SURFACE ALBEDO **

- Overall the NARR and NLDAS atmospheric forcing compared well with the *in situ* data.  However, the incident shortwave radiation (IS) produced the highest errors.  The figure to the right shows the “Golden Days” for the CEOP Sites for Incoming shortwave radiation and net radiation.  The golden days are defined as the ten days in each month with the largest daily mean radiation. Among the golden days, the incoming shortwave radiation showed a closer agreement to the in-situ measurements with high correlation (R>0.97) and less relative bias error (7.6% for NLDAS Mosaic, 0.8% for NLDAS Noah, and 12.7% for NARR Noah).  The LW upwelling and downwelling radiation generally correspond with good agreement to the reference data (not shown).  Similarly, good agreement is shown with the precipitation data (not shown) and net radiation (lower,right).**  **

- 1) CEOP 3&4 Test Sites (Oct. 2002 – Dec. 2004, 1⁄2 hr)
- 2) Oklahoma Mesonet (1994-Present) OASIS Super Sites (1999-2003)
- 3) Middle Rio Grande (ET Improvement, Noah, CLM-2) (2003-Present)

- CEOP STUDY SITES

- NARR & NLDAS DATA SET SUMMARY

**1) NARR**: North America Regional Reanalysis (NOAA/NCEP)
-      - Coupled land-atmosphere model, 30 Pressure Levels, 196 Variables
-      - 1979-2003, 32km spatial resolution with 3-hour output frequency
-      - Noah Land Surface Model
**2)  NLDAS at NOAA/NCEP & NASA/GSFC**
**     **- Real time and retrospective hourly, uncoupled land surface models
-      - Spatially 1/8th-degree grid (~13km), 1-hour frequency, 10/1996 to present
-      -	UMD Pinker’s corrected **GOES**-based radiation forcing
-      - NOAA NCEP: Noah Land Surface Model
-      - NASA/GSFC: Mosaic Land Surface Model

**Figure **shows the spatial distribution and the remote sensing land cover map at 1km spatial resolution in the South Great Plain region derived in the University of Maryland.

**Golden Days Incoming**
** ****Incident SW (IS) Radiation (Top) and Net Radiation (Bottom****)**

- NASA LAND INFORMATION SYSTEM

**Assimilated “Observed” albedo (in situ) in LIS Mosaic model **
-  In situ albedo directly assimilated (inserted) for each step
-  Snow albedo not in Mosaic LSM but in Observed data (see Spikes)
-  Improved accuracy of heat fluxes (shown SH+LH+G)
-  Current work inserting *in situ* soil moisture.

- Surface Albedo – In Situ vs. Model

**Surface Heat Flux Improvement**

- LAND COVER

**Updated NLDAS Land Cover**
-  3 (E4, E7 & E20) of 12 Sites did not match in-situ cover
-  Updated Dominant UMD Land Cover in NLDAS
-  Significant improvements in Heat Fluxes
-  Mosaic and CLM2 are more sensitive to Land Cover

**STUDY SITES**

**IS**

**Ppt**

**SH+LH RMS Error**

**Mosaic**

**Noah**

**CLM-2**

**IS**

**Rn**

-  **Model Comparisons with CEOP Site Data show NLDAS Mosaic and NARR Noah LSMs underestimates Latent Heat (LH) and overestimates Sensible Heat (SH) Fluxes During a summer dry down period starting in July.  ****NLDAS Noah LSM shows relatively less biases in both Sensible and Latent Heat Fluxes.**

**NOAA & NASA Multi-Model Intercomparison for CEOP Sites**

**Future Directions**

**We are currently extending this study in to the semi-arid southwest area of the Middle Rio Grande in New Mexico, US and the Oklahoma Mesonet.  Future plans are to further use multi-model capability of the Land Information System (LIS) to provide mid-range (<14-days) **_**ensemble**_** hydrologic predictions from NOAA and NASA precipitation and temperature forecast data.  We also plan to further develop objective model parameter estimates for the LSMs. Another key objective is to further assimilate satellite derived data in to the land surface modeling.**

**Precipitation & **
**Shortwave Radiation**

**NLDAS-Mosaic**

**NLDAS-Noah**

**NARR-Noah**