Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center develops new products and techniques that can be used in operational meteorology. The majority of these products are derived from NASA polar-orbiting satellite imagery from the Earth Observing System (EOS) platforms. One such product is a Greenness Vegetation Fraction (GVF) dataset, which is produced from Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the new SPoRT-MODIS GVF dataset on land surface models apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. The second phase of the project is to examine the impacts of the SPoRT GVF dataset on NWP using the Weather Research and Forecasting (WRF) model. Two separate WRF model simulations were made for individual severe weather case days using the NCEP GVF (control) and SPoRT GVF (experimental), with all other model parameters remaining the same. Based on the sensitivity results in these case studies, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). The opposite was true for areas with lower GVF in the SPoRT model runs. These differences in the heating and evaporation rates produced subtle yet quantifiable differences in the simulated convective precipitation systems for the selected severe weather case examined

Evaluating the Impacts of NASA/SPoRT Daily Greenness Vegetation Fraction on Land Surface Model and Numerical Weather Forecasts

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed a Greenness Vegetation Fraction (GVF) dataset, which is updated daily using swaths of Normalized Difference Vegetation Index data from the Moderate Resolution Imaging Spectroradiometer (MODIS) data aboard the NASA EOS Aqua and Terra satellites. NASA SPoRT began generating daily real-time GVF composites at 1-km resolution over the Continental United States (CONUS) on 1 June 2010. The purpose of this study is to compare the National Centers for Environmental Prediction (NCEP) climatology GVF product (currently used in operational weather models) to the SPoRT-MODIS GVF during June to October 2010. The NASA Land Information System (LIS) was employed to study the impacts of the SPoRT-MODIS GVF dataset on a land surface model (LSM) apart from a full numerical weather prediction (NWP) model. For the 2010 warm season, the SPoRT GVF in the western portion of the CONUS was generally higher than the NCEP climatology. The eastern CONUS GVF had variations both above and below the climatology during the period of study. These variations in GVF led to direct impacts on the rates of heating and evaporation from the land surface. In the West, higher latent heat fluxes prevailed, which enhanced the rates of evapotranspiration and soil moisture depletion in the LSM. By late Summer and Autumn, both the average sensible and latent heat fluxes increased in the West as a result of the more rapid soil drying and higher coverage of GVF. The impacts of the SPoRT GVF dataset on NWP was also examined for a single severe weather case study using the Weather Research and Forecasting (WRF) model. Two separate coupled LIS/WRF model simulations were made for the 17 July 2010 severe weather event in the Upper Midwest using the NCEP and SPoRT GVFs, with all other model parameters remaining the same. Based on the sensitivity results, regions with higher GVF in the SPoRT model runs had higher evapotranspiration and lower direct surface heating, which typically resulted in lower (higher) predicted 2-m temperatures (2-m dewpoint temperatures). Portions of the Northern Plains states experienced substantial increases in convective available potential energy as a result of the higher SPoRT/MODIS GVFs. These differences produced subtle yet quantifiable differences in the simulated convective precipitation systems for this event

Recent Upgrades to NASA SPoRT Initialization Datasets for the Environmental Modeling System

The NASA Short-term Prediction Research and Transition (SPoRT) Center has developed several products for its NOAA/National Weather Service (NWS) partners that can initialize specific fields for local model runs within the NOAA/NWS Science and Training Resource Center Environmental Modeling System (EMS). The suite of SPoRT products for use in the EMS consists of a Sea Surface Temperature (SST) composite that includes a Lake Surface Temperature (LST) analysis over the Great Lakes, a Great Lakes sea-ice extent within the SST composite, a real-time Green Vegetation Fraction (GVF) composite, and NASA Land Information System (LIS) gridded output. This paper and companion poster describe each dataset and provide recent upgrades made to the SST, Great Lakes LST, GVF composites, and the real-time LIS runs

A Real-Time MODIS Vegetation Composite for Land Surface Models and Short-Term Forecasting

The NASA Short-term Prediction Research and Transition (SPoRT) Center is producing real-time, 1- km resolution Normalized Difference Vegetation Index (NDVI) gridded composites over a Continental U.S. domain. These composites are updated daily based on swath data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor aboard the polar orbiting NASA Aqua and Terra satellites, with a product time lag of about one day. A simple time-weighting algorithm is applied to the NDVI swath data that queries the previous 20 days of data to ensure a continuous grid of data populated at all pixels. The daily composites exhibited good continuity both spatially and temporally during June and July 2010. The composites also nicely depicted high greenness anomalies that resulted from significant rainfall over southwestern Texas, Mexico, and New Mexico during July due to early-season tropical cyclone activity. The SPoRT Center is in the process of computing greenness vegetation fraction (GVF) composites from the MODIS NDVI data at the same spatial and temporal resolution for use in the NASA Land Information System (LIS). The new daily GVF dataset would replace the monthly climatological GVF database (based on Advanced Very High Resolution Radiometer [AVHRR] observations from 1992-93) currently available to the Noah land surface model (LSM) in both LIS and the public version of the Weather Research and Forecasting (WRF) model. The much higher spatial resolution (1 km versus 0.15 degree) and daily updates based on real-time satellite observations have the capability to greatly improve the simulation of the surface energy budget in the Noah LSM within LIS and WRF. Once code is developed in LIS to incorporate the daily updated GVFs, the SPoRT Center will conduct simulation sensitivity experiments to quantify the impacts and improvements realized by the MODIS real-time GVF data. This presentation will describe the methodology used to develop the 1-km MODIS NDVI composites and show sample output from summer 2010, compare the MODIS GVF data to the AVHRR monthly climatology, and illustrate the sensitivity of the Noah LSM within LIS and/or the coupled LIS/WRF system to the new MODIS GVF dataset

P69 Using the NASA-Unified WRF to Assess the Impacts of Real-Time Vegetation on Simulations of Severe Weather

Since June 2010, the NASA Short-term Prediction Research and Transition (SPoRT; Goodman et al. 2004; Darden et al. 2010; Stano et al. 2012; Fuell et al. 2012) Center has been generating a real-time Normalized Difference Vegetation Index (NDVI) and corresponding Green Vegetation Fraction (GVF) composite based on reflectances from NASA s Moderate Resolution Imaging Spectroradiometer (MODIS) instrument. This dataset is generated at 0.01 resolution across the Continental United States (CONUS), and updated daily. The goal of producing such a vegetation dataset is to improve over the default climatological GVF dataset in land surface and numerical weather prediction models, in order to have better simulations of heat and moisture exchange between the land surface and the planetary boundary layer. Details on the SPoRT/MODIS vegetation composite algorithm are presented in Case et al. (2011). Vegetation indices such as GVF and Leaf Area Index (LAI) are used by land surface models (LSMs) to represent the horizontal and vertical density of plant vegetation (Gutman and Ignatov 1998), in order to calculate transpiration, interception and radiative shading. Both of these indices are related to the NDVI; however, there is an inherent ambiguity in determining GVF and LAI simultaneously from NDVI, as described in Gutman and Ignatov (1998). One practice is to specify the LAI while allowing the GVF to vary both spatially and temporally, as is done in the Noah LSM (Chen and Dudhia 2001; Ek et al. 2003). Operational versions of Noah within several of the National Centers for Environmental Prediction (NCEP) global and regional modeling systems hold the LAI fixed, while the GVF varies according to a global monthly climatology. This GVF climatology was derived from NDVI data on the NOAA Advanced Very High Resolution Radiometer (AVHRR) polar orbiting satellite, using information from 1985 to 1991 (Gutman and Ignatov 1998; Jiang et al. 2010). Representing data at the mid-point of every month, the climatological dataset is on a grid with 0.144 (~16 km) spatial resolution and is distributed with the community WRF model (Ek et al. 2003; Jiang et al. 2010; Skamarock et al. 2008)

Quantifying numerical weather and surface model sensitivity to land use and land cover changes

Land surfaces have changed as a result of human and natural processes, such asdeforestation, urbanization, desertification and natural disasters like wildfires. Land use and landcover change impacts local and regional climates through various bio geophysical processes acrossmany time scales. More realistic representation of land surface parameters within the land surfacemodels are essential to for climate models to accurately simulate the effects of past, current andfuture land surface processes. In this study, we evaluated the sensitivity and accuracy of theWeather Research and Forecasting (WRF) model though the default MODIS land cover data andannually updated land cover data over southeast of United States. Findings of this study indicatedthat the land surface fluxes, and moisture simulations are more sensitive to the surfacecharacteristics over the southeast US. Consequently, we evaluated the WRF temperature andprecipitation simulations with more accurate observations of land surface parameters over thestudy area. We evaluate the model performance for the default and updated land cover simulationsagainst observational datasets. Results of the study showed that updating land cover resulted insubstantial variations in surface heat fluxes and moisture balances. Despite updated land use andland cover data provided more representative land surface characteristics, the WRF simulated 2- m temperature and precipitation did not improved due to use of updated land cover data. Further,we conducted machine learning experiments to post-process the Noah-MP land surface modelsimulations to determine if post processing the model outputs can improve the land surfaceparameters. The results indicate that the Noah-MP simulations using machine learning remarkablyimproved simulation accuracy and gradient boosting, and random forest model had smaller meanerror bias values and larger coefficient of determination over the majority of stations. Moreover,the findings of the current study showed that the accuracy of surface heat flux simulations byNoah-MP are influenced by land cover and vegetation type

Assessment of SMOS Soil Moisture Retrieval Parameters Using Tau-Omega Algorithms for Soil Moisture Deficit Estimation

Soil Moisture and Ocean Salinity (SMOS) is the latest mission which provides flow of coarse resolution soil moisture data for land applications. However, the efficient retrieval of soil moisture for hydrological applications depends on optimally choosing the soil and vegetation parameters. The first stage of this work involves the evaluation of SMOS Level 2 products and then several approaches for soil moisture retrieval from SMOS brightness temperature are performed to estimate Soil Moisture Deficit (SMD). The most widely applied algorithm i.e. Single channel algorithm (SCA), based on tau-omega is used in this study for the soil moisture retrieval. In tau-omega, the soil moisture is retrieved using the Horizontal (H) polarisation following Hallikainen dielectric model, roughness parameters, Fresnel's equation and estimated Vegetation Optical Depth (tau). The roughness parameters are empirically calibrated using the numerical optimization techniques. Further to explore the improvement in retrieval models, modifications have been incorporated in the algorithms with respect to the sources of the parameters, which include effective temperatures derived from the European Center for Medium-Range Weather Forecasts (ECMWF) downscaled using the Weather Research and Forecasting (WRF)-NOAH Land Surface Model and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) while the s is derived from MODIS Leaf Area Index (LAI). All the evaluations are performed against SMD, which is estimated using the Probability Distributed Model following a careful calibration and validation integrated with sensitivity and uncertainty analysis. The performance obtained after all those changes indicate that SCA-H using WRF-NOAH LSM downscaled ECMWF LST produces an improved performance for SMD estimation at a catchment scale

Satellite and in situ observations for advancing global Earth surface modelling: a review

In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort