SeaWiFS offered a good view of the Yellow Sea and the East China Sea with its prominent Yangtze River plume. Haze from spring dust storms and pollution covers much of the region. Increasing use of coal and wood for heating often leads to widespread haze conditions. A higher resolution image can be found at S L1A_HROC.YellowSeaHaze.png Yellow Sea Haze captured by SeaWiFS Gene Feldman NASA GSFC, Laboratory for Hydrospheric Processes Office for Global Carbon Studies

Yellow Sea Haze captured by SeaWiFS

Ocean Salinity Model Function During the late seventies research was conducted to define the model for the complex dielectric constant of sea water as a function of temperature and salinity at long wavelengths (1-3 GHz). The motivation was remote sensing of ocean salinity. Now, a quarter century later during which remote sensing of salinity has been successfully demonstrated with aircraft instruments, the issue is being revisited. The motivation this time is Aquarius and SMOS soon to be deployed to map the global salinity field. The contemporary issue is the goal of psu which is almost an order of magnitude improvement compared to the early airborne sensors. This is a severe requirement on all aspects of the remote sensing problem, including the model function, ε(S,T). Among the issues that need to be addressed are: Units of Salinity: The definition of salinity has changed and a new scale (psu) based on conductivity has been adopted. The Model Function Itself: The original model function was based on a parameterization of the coefficients in the Debye function fitted to measurements on NaCl solutions with rather limited data near the frequency (1.413 GHz) used by the sensors. Recent measurements at this frequency indicate refinement is warranted. Accuracy: The modern science goal is psu is an order of magnitude smaller than in the past. These issues are illustrated in figure which shows the real and imaginary part of ε(S,T) as a function of frequency at a salinity of 25 psu and temperature of 25C. The dashed curve is the original model function (by Klein-Swift) and the solid curve is that developed recently by Ellison et al. The differences, although small on the scale presented, are much to large at L-band to achieve the science goal of 0.2 psu. Recently teams at the Polytechnic University of Catalonya (UPC) and the George Washington University(GWU working with GSFC] made measurements at the desired frequency (1.413 GHz) to compare with the existing models for ε(S,T). The data appear to be closer to the Klein-Swift model than the Ellison model but have differences among themselves that are significant at the accuracy desired of future sensors. The measurements suggest more work is needed and motivated a workshop to develop a coordinated plan of action. This workshop was held in March, 2004, at NRL at the Stennis Space Center. David M. Le Vine NASA GSFC, Laboratory for Hydrospheric Processes, Microwave Sensors Branch

Ocean Salinity Model Function Model Function: Expression for the dielectric constant of sea water as a function of temperature and salinity. –Issue: Current expression was developed a quarter century ago with sparse data. –Today: Revisit with modern measurements. –Motivation: Aquarius and SMOS soon to be deployed to map the global salinity field. Progress: Workshop at NRL/Stennis Space Center: –Reviewed progress and set goals for research –Plan: GSFC/GWU and UPC measurement teams to cooperate in establishing a modern model function suitable for Aquarius and SMOS. Conclusion: Differences exist which exceed acceptable tolerances –Goal: Remote sensing accuracy of 0.1 psu. –Need: Modern measurements with high precision. Comparison of existing model function (dashed) with recent measurements by Ellison et al (solid) for a salinity of 25 psu and temperature of 25 C.

High Resolution Global Modeling with the Land Information System Christa Peters-Lidard*, Paul Houser/974 Sujay Kumar, Yudong Tian/974/GEST Jim Geiger, Susan Olden, Luther Lighty/580 *NASA GSFC, Laboratory for Hydrospheric Processes, Hydrological Sciences Branch Objective: A high performance, high resolution (1 km) global land modeling and assimilation system. Applications: Weather and climate model initialization and retrospective coupled modeling, Flood and water resources forecasting, Precision agriculture, Mobility assessment, etc. 1km MODIS Leaf Area Index (LAI) data

Key LIS Milestones Complete Aug. 2002: Install LIS Cluster at GSFC –200 nodes, 112 GB total memory, 22 TB total disk Mar. 2003: First code improvement –Implement global LIS at 5 km resolution May 2004: Second code improvement –Implement global LIS at 1 km resolution Future Jul. 2004: Interoperability demonstration –Implement LIS as a partially** ESMF-compliant land model component Aug. 2004: Customer delivery –Deliver LIS to customers at GMAO, NCEP, Princeton, COLA, CSU, UAZ, etc. **July 2004 Milestone renegotiated due to delays in ESMF project

LIS Performance and Scaling Simulation: one day, 15 minute time step b. Scaling curves for CLM and Noah at 1km resolution a. Performance improvement on single CPU at ¼ o resolution

Evaluation of LIS 1km Inputs and Outputs Example: Longwave Radiation and Latent Heat Fluxes July 1 – Sept 30, 2001, Errors across all CEOP Reference Sites Location map and sample latent heat flux output for June 1, 2001