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Advancing the Effectiveness and Efficiency of GLDAS Assimilation of JPSS Land Data Products for NCEP NWP and Drought Monitoring Operations

For MAPP program Competition 1: Advancing Earth System Data Assimilation, we
propose to advance the effectiveness and efficiency of assimilation of JPSS land data products
through the Global Land Data Assimilation System (GLDAS) for NCEP NWP and drought
monitoring operations. Satellite remote sensing land surface data products have been generated
and made available for various applications in the past decades. The data products of soil
moisture, land surface temperature, albedo, vegetation indices/green vegetation fraction, surface
type are currently operationally generated from NOAA JPSS VIIRS or GCOM-W1/AMSR2
satellite sensors for use in NOAA NCEP numerical weather, climate and hydrological
predictions. Several algorithms have also been introduced to assimilate these data products into
land surface models to improve NCEP weather, climate and hydrological forecasts. However,
none of those JPSS land data products and none of those data assimilation algorithms were used
in NCEP operational NWP models, drought monitoring system, or national water model. The
situation might have been caused by the low efficiency and effectiveness of the currently used
data assimilation algorithms. Based on the findings of the land data assimilation research
community in the past decades, we propose to: 1) implement the dual-pass data assimilation
algorithm by Yang et al (2007) into GLDAS of NCEP without the need to use the so-called
“bias-correction” in the conventional land data assimilation algorithms; 2) inter-compare the
efficiency and effectiveness of the dual-pass data assimilation algorithm with conventional
algorithm (i.e., the EnKF using CDF-matching for bias correction); 3) evaluate the impact of the
dual-pass algorithm in NCEP NWP model simulations and forecasts; 4) examine the drought
monitoring capability using soil moisture profile output from GLDAS with or without
assimilating the satellite land data products; 5) streamline the dual-pass data assimilation
algorithm in GLDAS to enable a straightforward transition of the algorithm to NCEP operation
environment and finally deliver the improved drought monitoring products to NCEP.

Through advancing CPO’s capability of “modeling and prediction” and meeting NOAA’s
long-term climate goal, this proposed project attempts to enhance the effectiveness and
efficiency of assimilation of NOAA JPSS data products in NCEP’s modeling and prediction
systems of GLDAS, Global Forecast System (GFS), Climate Forecast System (CFS) and drought
monitoring operations. This attempt meets CPO’s research objective of focusing on climate
intelligence and climate resilience as stated in the MAPP FY18 information sheet.

Climate Risk Area: Water Resources

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