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The North American Land Data Assimilation System (N-LDAS) Project: Validation of the Energy Budget Components Eric F. Wood1, Lifeng Luo2, Jesse Meng3, Fenghua Wen1, Rachel Pinker4, Dan Tarpley5, Alan Robock2, Justin Sheffield1, Kenneth Mitchell3, Paul R. Houser6, John Schaake7, Dennis Lettenmaier8, Brian Cosgrove6, Qingyun Duan7, Dag Lohmann3, Wayne Higgins9 1Department of Civil Engineering, Princeton University 2Department of Environmental Sciences, Rutgers University 3NOAA/NWS/NCEP/EMC 4Department of Meteorology, University of Maryland 5NOAA/NESDIS/ORA 6Hydrological Sciences Branch, NASA/GSFC 7NOAA/NWS/OHD 8Department of Civil and Environmental Engineering, University of Washington 9NOAA/NWS/NCEP/CPC GCIP/GAPP Investigator Meeting New Orleans, May 2002 LDAS Goals and Energy Budget Validation 1) Improve LSM physics by sharing methodologies and data sources; Comparisons of energy states and fluxes with observations 2) Identify causes of the spread in magnitudes of surface fluxes and states typically seen in LSM intercomparisons; Try to distinguish between errors in forcings and differences in model predictions 3) Compare land states of the uncoupled LDAS with traditional coupled 4DDA Compare forcings from EDAS with observations 4) Provide land-state initial conditions land-memory predictability studies and real-time 4DDA forecasting. Estimate the errors in the LDAS predictions LDAS Energy Balance Validation Design 1. Model Energy Forcings. Incoming Solar Radiation (NESDIS 0.5-degree, hourly GOES solar insolation or EDAS is compared with stations from NOAA’s SURFRAD, Oklahoma Mesonet and ARM/CART) Downward Longwave Radiation (Eta model-estimated longwave is compared with measurements ARM/CART) 2. Model-predicted Energy States. Skin temperature (Compared with measurements NESDIS 0.5-degree, hourly GOES skin temperature for clear sky areas, comparisons with SURFRAD) Surface heat fluxes (Compared with ARM/CART EBBR measurements) Validation of Model Energy Forcing Data Sources 1.LDAS (EDAS-model) insolation data 2.GOES-based 0.5 degree resolution insolation data 3.Insolation data from 6 sites in SURFRAD network 4.Data from 115 sites in OK Mesonet network 5.Data from ARM/CART sites Time: 04/1999 – 09/1999 SURFRAD Sites Oklahoma Mesonet Sites Forcing Validation: SURFRAD Monthly mean diurnal solar insolation intercomparison (GOES, EDAS vs. SURFRAD) May 1999 Notice EDAS phasing problem Forcing Validation: SURFRAD Monthly mean diurnal solar insolation intercomparison (GOES, EDAS vs. SURFRAD) May 2000 Notice EDAS phasing problem resolved Forcing Validation: ARM/CART Monthly Averaged Hourly Monthly Averaged Hourly All-Sky SW [Wm-2] All-Sky SW [Wm-2] Summer (MJJA) Winter (NDJF) UMD/SRB UMD/SRB RMS BIAS n RMS BIAS n 40 27 46 24 11 30 ARM/SGP ARM/SGP Downward Shortwave: OK Mesonet hourly Solar Insolation ACME station Comparisons (Jan98-Sep99) carried out for all the Oklahoma daily 5-day mesonet stations (Jan 98-Sep 99) 15-day monthly Downward Longwave: ARM/CART Validation of Model Energy Components Data: 14 ARM/CART EBBR stations Time: 10/01/1997 - 09/30/1999 Skin Temperature ( GOES-NOAH ) October 2001 Region 2 Region 5 Skin Temperature ( GOES-VIC ) July 1999 Northern mountain region for 07/1999 Comparisons of surface temperature between VIC and GOES GOES Surface Temperature Data Sources: GOES (NESDIS, 0.5o, hourly, clear sky) Six regions (14ox19.4o) in continental USA. GOES Surface Temperature Soil Temperature Surface Fluxes (VIC) Validation Improvements (VIC) Diagnostic analysis of the initial results lead to re-calibration with more realistic vegetation parameters. Conclusions 1. Considerable validation efforts are required to quality control the operational data stream for the forcing data. More efforts (and problems) were encountered than originally expected. 2. The community must spend more efforts in validating soil and vegetation classifications, especially for croplands. 3. Validation programs can be used to identify where model parameters (and processes) can be improved.
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