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 Sensitivity of the NCEP/Noah land surface model to the MODIS green vegetation fraction data set

2006 ◽  
Vol 33 (13) ◽  
Author(s):  
Jesse Miller ◽  
Michael Barlage ◽  
Xubin Zeng ◽  
Helin Wei ◽  
Kenneth Mitchell ◽  
...  
Keyword(s):  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Data Set ◽  
Vegetation Fraction ◽  
Green Vegetation ◽  
Noah Land Surface Model

Evaluation of the Noah Land Surface Model Using Data from a Fair-Weather IHOP_2002 Day with Heterogeneous Surface Fluxes

2008 ◽  
Vol 136 (12) ◽  
pp. 4915-4941 ◽  
Author(s):  
Margaret A. LeMone ◽  
Mukul Tewari ◽  
Fei Chen ◽  
Joseph G. Alfieri ◽  
Dev Niyogi
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Minor Effect ◽  
Near Surface ◽  
Vegetation Fraction ◽  
A Value ◽  
Green Vegetation ◽  
Noah Land Surface Model

Abstract Sources of differences between observations and simulations for a case study using the Noah land surface model–based High-Resolution Land Data Assimilation System (HRLDAS) are examined for sensible and latent heat fluxes H and LE, respectively; surface temperature Ts; and vertical temperature difference T0 − Ts, where T0 is at 2 m. The observational data were collected on 29 May 2002, using the University of Wyoming King Air and four surface towers placed along a sparsely vegetated 60-km north–south flight track in the Oklahoma Panhandle. This day had nearly clear skies and a strong north–south soil-moisture gradient, with wet soils and widespread puddles at the south end of the track and drier soils to the north. Relative amplitudes of H and LE horizontal variation were estimated by taking the slope of the least squares best-fit straight line ΔLE/ΔH on plots of time-averaged LE as a function of time-averaged H for values along the track. It is argued that observed H and LE values departing significantly from their slope line are not associated with surface processes and, hence, need not be replicated by HRLDAS. Reasonable agreement between HRLDAS results and observed data was found only after adjusting the coefficient C in the Zilitinkevich equation relating the roughness lengths for momentum and heat in HRLDAS from its default value of 0.1 to a new value of 0.5. Using C = 0.1 and adjusting soil moisture to match the observed near-surface values increased horizontal variability in the right sense, raising LE and lowering H over the moist south end. However, both the magnitude of H and the amplitude of its horizontal variability relative to LE remained too large; adjustment of the green vegetation fraction had only a minor effect. With C = 0.5, model-input green vegetation fraction, and our best-estimate soil moisture, H, LE, ΔLE/ΔH, and T0 − Ts, were all close to observed values. The remaining inconsistency between model and observations—too high a value of H and too low a value of LE over the wet southern end of the track—could be due to HRLDAS ignoring the effect of open water. Neglecting the effect of moist soils on the albedo could also have contributed.

Impact of GVF Derivation Methods on Noah Land Surface Model Simulations and WRF Model Forecasts

2018 ◽  
Vol 19 (12) ◽  
pp. 1917-1933 ◽  
Author(s):  
Li Fang ◽  
Xiwu Zhan ◽  
Christopher R. Hain ◽  
Jifu Yin ◽  
Jicheng Liu
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Vegetation Fraction ◽  
Data Products ◽  
Noah Land Surface Model

Abstract Green vegetation fraction (GVF) plays a crucial role in the atmosphere–land water and energy exchanges. It is one of the essential parameters in the Noah land surface model (LSM) that serves as the land component of a number of operational numerical weather prediction models at the National Centers for Environmental Prediction (NCEP) of NOAA. The satellite GVF products used in NCEP models are derived from a simple linear conversion of either the normalized difference vegetation index (NDVI) from the Advanced Very High Resolution Radiometer (AVHRR) currently or the enhanced vegetation index (EVI) from the Visible Infrared Imaging Radiometer Suite (VIIRS) planned for the near future. Since the NDVI or EVI is a simple spectral index of vegetation cover, GVFs derived from them may lack the biophysical meaning required in the Noah LSM. Moreover, the NDVI- or EVI-based GVF data products may be systematically biased over densely vegetated regions resulting from the saturation issue associated with spectral vegetation indices. On the other hand, the GVF is physically related to the leaf area index (LAI), and thus it could be beneficial to derive GVF from LAI data products. In this paper, the EVI-based and the LAI-based GVF derivation methods are mathematically analyzed and are found to be significantly different from each other. Impacts of GVF differences on the Noah LSM simulations and on weather forecasts of the Weather Research and Forecasting (WRF) Model are further assessed. Results indicate that LAI-based GVF outperforms the EVI-based one when used in both the offline Noah LSM and WRF Model.

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 Analysis of the Impact of Snow on Daily Weather Variability in Mountainous Regions Using MM5

2007 ◽  
Vol 8 (2) ◽  
pp. 245-258 ◽  
Author(s):  
Jiming Jin ◽  
Norman L. Miller
Keyword(s):  
Skin Temperature ◽  
Sierra Nevada ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Weather Variability ◽  
Warm Bias ◽  
Daily Weather ◽  
Vegetation Fraction ◽  
The Impact

Abstract The impacts of snow on daily weather variability, as well as the mechanisms of snowmelt over the Sierra Nevada, California–Nevada, mountainous region, were studied using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) forced by 6-h reanalysis data from the National Centers for Environmental Prediction. The analysis of two-way nested 36–12-km MM5 simulations during the 1998 snowmelt season (April–June) shows that the snow water equivalent (SWE) is underestimated when there are conditions with higher temperature and greater precipitation than observations. An observed daily SWE dataset derived from the snow telemetry network was assimilated into the Noah land surface model within MM5. This SWE assimilation reduces the warm bias. The reduction of the warm bias results from suppressed upward sensible heat flux caused by the decreased skin temperature. This skin temperature reduction is the result of the longer assimilated snow duration than in the model run without SWE assimilation. Meanwhile, the cooled surface leads to a more stable atmosphere, resulting in a decrease in the exaggerated precipitation. Additionally, the detailed analysis of the snowmelt indicates that the absence of vegetation fraction in the most sophisticated land surface model (Noah) in the MM5 package results in an overestimation of solar radiation reaching the snow surface, giving rise to heavier snowmelt. An underestimated surface albedo also weakly contributes to the stronger snowmelt. The roles of the vegetation fraction and albedo in snowmelt are further verified by an additional offline simulation from a more realistic land surface model with advanced snow and vegetation schemes forced by the MM5 output. An improvement in SWE description is clearly seen in this offline simulation over the Sierra Nevada region.

scholarly journals Continuous high resolution mid-latitude belt simulations for July–August 2013 with WRF

2016 ◽  
Author(s):  
Thomas Schwitalla ◽  
Hans-Stefan Bauer ◽  
Volker Wulfmeyer ◽  
Kirsten Warrach-Sagi
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Wrf Model ◽  
Land Surface Model ◽  
Sea Surface ◽  
Surface Model ◽  
Latitude Belt ◽  
Noah Land Surface Model ◽  
The Impact

Abstract. The impact of a convection permitting (CP) northern hemisphere latitude-belt simulation with the Weather Research and Forecasting (WRF) model was investigated during the July and August 2013. For this application, the WRF model together with the NOAH land-surface model (LSM) was applied at two different horizontal resolutions, 0.03° (HIRES) and 0.12° (LOWRES). The set-up as a latitude-belt domain avoids disturbances that originate from the western and eastern boundaries and therefore allows to study the impact of model resolution and physical parameterizations on the results. Both simulations were forced by ECMWF operational analysis data at the northern and southern domain boundaries and the high-resolution Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) data at the sea surface. The simulations are compared to the operational ECMWF analysis for the representation of large scale features. To compare the simulated precipitation, the operational ECMWF forecast, the CPC MORPHing (CMORPH), and the ENSEMBLES gridded observation precipitation data set (E-OBS) were used. Compared to the operational high-resolution ECMWF analysis, both simulations are able to capture the large scale circulation pattern though the strength of the Pacific high is considerably overestimated in the LOWRES simulation. Major differences between ECMWF and WRF occur during July 2013 when the lower resolution simulation shows a significant negative bias over the North Atlantic which is not observed in the CP simulation. The analysis indicates deficiencies in the applied combinations of cloud microphysics and convection parametrization on the coarser grid scale in subpolar regions. The overall representation of the 500 hPa geopotential height surface is also improved by the CP simulation compared to the LOWRES simulation apart across Newfoundland where the geopotential height is higher than in the LOWRES simulation due to a northward shift of the location of the Atlantic high pressure system. Both simulations show higher wind speeds in the boundary layer by about 1.5 m s−1 compared to the the ECMWF analysis. Due to the higher surface evaporation, this results in a moist bias of 0.5 g kg−1 at 925 hPa in the planetary boundary layer compared to the ECMWF analysis. Major differences between ECMWF and WRF occur in the simulation of the 2-m temperatures over the Asian desert and steppe regions. They are significantly higher in WRF by about 5 K both during day- and night-time presumably as a result of different soil hydraulic parameters used in the NOAH land surface model for steppe regions. The precipitation of the HIRES simulation shows a better spatial agreement with CMORPH especially over mountainous terrain. The overall bias reduces from 80 mm at the coarser resolution to 50 mm in the HIRES simulation and the root mean square error is reduced by about 35 % when compared to the CMORPH precipitation analysis. The precipitation distribution agrees much better with the CMORPH data than the LOWRES simulation which tends to overestimate precipitation, mainly caused by the convection parametrization. Especially over Europe the CP resolution reduces the precipitation bias by about 30 % to 20 mm as a result of a better terrain representation and due to the avoidance of the convection parameterization.

scholarly journals Correction of real-time satellite precipitation with satellite soil moisture observations

2015 ◽  
Vol 19 (10) ◽  
pp. 4275-4291 ◽  
Author(s):  
W. Zhan ◽  
M. Pan ◽  
N. Wanders ◽  
E. F. Wood
Keyword(s):  
Soil Moisture ◽  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Normal Surface ◽  
Infiltration Capacity ◽  
Satellite Precipitation ◽  
The Impact

Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the onset of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. The assimilation is most effective in the correction of medium rainfall under dry to normal surface conditions, while limited/negative improvement is seen over wet/saturated surfaces. On the other hand, high-frequency noises in satellite soil moisture impact the assimilation by increasing rainfall frequency. The noise causes larger uncertainty in the false-alarmed rainfall over wet regions. A threshold of 2 mm day−1 soil moisture change is identified and applied to the assimilation, which masked out most of the noise.

scholarly journals Improving evapotranspiration in a land surface model using biophysical variables derived from MSG/SEVIRI satellite

2012 ◽  
Vol 16 (8) ◽  
pp. 2567-2583 ◽  
Author(s):  
N. Ghilain ◽  
A. Arboleda ◽  
G. Sepulcre-Cantò ◽  
O. Batelaan ◽  
J. Ardö ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Real Time ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Continental Scale ◽  
Western Africa ◽  
Large Gradient ◽  
The Impact

Abstract. Monitoring evapotranspiration over land is highly dependent on the surface state and vegetation dynamics. Data from spaceborn platforms are desirable to complement estimations from land surface models. The success of daily evapotranspiration monitoring at continental scale relies on the availability, quality and continuity of such data. The biophysical variables derived from SEVIRI on board the geostationary satellite Meteosat Second Generation (MSG) and distributed by the Satellite Application Facility on Land surface Analysis (LSA-SAF) are particularly interesting for such applications, as they aimed at providing continuous and consistent daily time series in near-real time over Africa, Europe and South America. In this paper, we compare them to monthly vegetation parameters from a database commonly used in numerical weather predictions (ECOCLIMAP-I), showing the benefits of the new daily products in detecting the spatial and temporal (seasonal and inter-annual) variability of the vegetation, especially relevant over Africa. We propose a method to handle Leaf Area Index (LAI) and Fractional Vegetation Cover (FVC) products for evapotranspiration monitoring with a land surface model at 3–5 km spatial resolution. The method is conceived to be applicable for near-real time processes at continental scale and relies on the use of a land cover map. We assess the impact of using LSA-SAF biophysical variables compared to ECOCLIMAP-I on evapotranspiration estimated by the land surface model H-TESSEL. Comparison with in-situ observations in Europe and Africa shows an improved estimation of the evapotranspiration, especially in semi-arid climates. Finally, the impact on the land surface modelled evapotranspiration is compared over a north–south transect with a large gradient of vegetation and climate in Western Africa using LSA-SAF radiation forcing derived from remote sensing. Differences are highlighted. An evaluation against remote sensing derived land surface temperature shows an improvement of the evapotranspiration simulations.

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