Impact of Real‐Time Satellite‐Derived Green Vegetation Fraction on National Water Model Fluxes in Humid Alabama Watersheds

2022 ◽  
Author(s):  
Nicholas J. Elmer ◽  
John R. Mecikalski ◽  
Andrew L. Molthan ◽  
David J. Gochis ◽  
Udaysankar Nair
Keyword(s):  
Real Time ◽  
Water Model ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
National Water

Influence of Near Real-Time Green Vegetation Fraction Data on the Numerical Weather Prediction in WRF over North China

2020 ◽  
Author(s):  
Bing Lu ◽  
Ji-Qin Zhong ◽  
Wei Wang ◽  
Shi-Hao Tang ◽  
Zhao-Jun Zheng
Keyword(s):  
Real Time ◽  
Numerical Weather Prediction ◽  
North China ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Numerical Weather ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
The Impact ◽  
Mean Square Errors

<p>Green vegetation fraction (GVF) has a prominent influence on the partitioning of surface sensible and latent heat fluxes in numerical weather prediction models. However, the multi-year monthly GVF climatology, which is the most commonly-used representation of vegetation states in models, has limited ability to capture the real-time vegetation status. In our study, a near real-time (NRT) GVF dataset generated from 8-day composite of the normalized difference vegetation index (NDVI) is compared with the 10-year averaged monthly GVF provided by the Weather Research and Forecasting (WRF) model. We examine the annual and inter-annual variability of the GVF over North China in details. Many differences of the GVF between the two datasets are found over the dryland cropland and grassland areas. Two experiments using different GVF datasets are performed to assess the impact of the GVF on the forecasts of screen-level temperature and humidity for one year. The results show that using the NRT GVF can lead to a widespread reduction of 2-m temperature in the order of 0.5 ℃, and an increase of 2-m humidity during the warm season. An evaluation against in-situ observations displays an overall positive impact on the near surface parameter forecasts. Over the dryland cropland and grassland areas, a quantitative validation shows that the root mean square errors of 24-h forecasts decline by 9%, 10% and 6% for 2-m temperature, 2-m specific humidity and 10-m wind speed, respectively, in May of 2012. Our study demonstrates that the NRT GVF can provide a more realistic representation of vegetation state which in turn helps to improve the short-range forecasts in the arid and semiarid regions of North China.</p>

scholarly journals Impact of Using Near Real-Time Green Vegetation Fraction in Noah Land Surface Model of NOAA NCEP on Numerical Weather Predictions

2018 ◽  
Vol 2018 ◽  
pp. 1-12
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jicheng Liu
Keyword(s):  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Set ◽  
Numerical Weather ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Noah Land Surface Model ◽  
The Impact

Green vegetation fraction (GVF) is one of the input parameters of the Noah land surface model (LSM) that is the land component of a number of operational numerical weather prediction (NWP) models at the National Centers for Environmental Prediction (NCEP) of NOAA. The Noah LSM in current NCEP operational NWP models has been using static multiyear averages of monthly GVF derived from satellite observations of NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index. The multiyear averages of GVF are evidently not the representative of actual conditions of the land surface vegetation cover. This study used a near-real-time (NRT) GVF data set generated from the 8-day composite of the leaf area index product from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess the impact of NRT GVF on off-line Noah LSM simulations and NWP forecast model. Simulations of the off-line Noah LSM in the Land Information System (LIS) and weather forecasts of the NASA-Unified Weather and Research Forecasting (NUWRF) were obtained using either the static multiyear average AVHRR GVF data set or the NRT MODIS GVF while meteorological forcing data and other settings were kept the same. The off-line simulations and WRF forecasts were then compared against in situ measurements or reanalysis products to assess the impact of using NRT GVF. Improvements of both soil moisture simulations as well as forecasts of 2-meter air temperature and humidity and precipitation from NUWRF were observed using the NRT GVF data products. The RMSE in SM estimates from the off-line Noah model is reduced by around 1.0% (1.41%) during the green-up phase and by 1.48% (2.24%) over the senescence phase for the surface (root zone) SM simulations. Around 82.3% validation sites (out of 1178 sites) showed positive impact on coupled WRF model with the insertion of NRT GVF.

scholarly journals Using a thermal-based two source energy balance model with time-differencing to estimate surface energy fluxes with day–night MODIS observations

2013 ◽  
Vol 17 (7) ◽  
pp. 2809-2825 ◽  
Author(s):  
R. Guzinski ◽  
M. C. Anderson ◽  
W. P. Kustas ◽  
H. Nieto ◽  
I. Sandholt
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
The United States ◽  
Surface Fluxes ◽  
Diurnal Changes ◽  
Energy Fluxes ◽  
Surface Energy Fluxes ◽  
Vegetation Fraction ◽  
Geostationary Satellites ◽  
Green Vegetation

Abstract. The Dual Temperature Difference (DTD) model, introduced by Norman et al. (2000), uses a two source energy balance modelling scheme driven by remotely sensed observations of diurnal changes in land surface temperature (LST) to estimate surface energy fluxes. By using a time-differential temperature measurement as input, the approach reduces model sensitivity to errors in absolute temperature retrieval. The original formulation of the DTD required an early morning LST observation (approximately 1 h after sunrise) when surface fluxes are minimal, limiting application to data provided by geostationary satellites at sub-hourly temporal resolution. The DTD model has been applied primarily during the active growth phase of agricultural crops and rangeland vegetation grasses, and has not been rigorously evaluated during senescence or in forested ecosystems. In this paper we present modifications to the DTD model that enable applications using thermal observations from polar orbiting satellites, such as Terra and Aqua, with day and night overpass times over the area of interest. This allows the application of the DTD model in high latitude regions where large viewing angles preclude the use of geostationary satellites, and also exploits the higher spatial resolution provided by polar orbiting satellites. A method for estimating nocturnal surface fluxes and a scheme for estimating the fraction of green vegetation are developed and evaluated. Modification for green vegetation fraction leads to significantly improved estimation of the heat fluxes from the vegetation canopy during senescence and in forests. When the modified DTD model is run with LST measurements acquired with the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Terra and Aqua satellites, generally satisfactory agreement with field measurements is obtained for a number of ecosystems in Denmark and the United States. Finally, regional maps of energy fluxes are produced for the Danish Hydrological ObsErvatory (HOBE) in western Denmark, indicating realistic patterns based on land use.

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