Peer review report 1 On “Improving Noah Land Surface Model Performance using Near Real Time Surface Albedo and Green Vegetation Fraction”

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.

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

Geophysical Research Letters ◽

10.1029/2006gl026636 ◽

2006 ◽

Vol 33 (13) ◽

Cited By ~ 37

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

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

Monthly Weather Review ◽

10.1175/2008mwr2354.1 ◽

2008 ◽

Vol 136 (12) ◽

pp. 4915-4941 ◽

Cited By ~ 58

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.

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

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0075.1 ◽

2018 ◽

Vol 19 (12) ◽

pp. 1917-1933 ◽

Cited By ~ 1

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.

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Parameterization of the snow-covered surface albedo in the Noah-MP Version 1.0 by implementing vegetation effects

Geoscientific Model Development ◽

10.5194/gmd-9-1073-2016 ◽

2016 ◽

Vol 9 (3) ◽

pp. 1073-1085 ◽

Cited By ~ 11

Author(s):

Sojung Park ◽

Seon Ki Park

Keyword(s):

Land Surface ◽

Surface Albedo ◽

Surface Condition ◽

Model Performance ◽

Land Surface Model ◽

Surface Model ◽

Vegetation Types ◽

Area Index ◽

Almost All ◽

Vegetation Effect

Abstract. Snow-covered surface albedo varies depending on many factors, including snow grain size, snow cover thickness, snow age, forest shading factor, etc., and its parameterization is still under great uncertainty. For the snow-covered surface condition, albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, stem and trunk in winter are not well represented with only these parameters. We found that such inadequate representation of the vegetation effect is mainly responsible for the large positive bias in calculating the winter surface albedo in the Noah-MP. In this study, we investigated the vegetation effect on the snow-covered surface albedo from observations and improved the model performance by implementing a new parameterization scheme. We developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the snow-covered surface albedo. As a result, the Noah-MP's performance in the winter surface albedo has significantly improved – the root mean square error is reduced by approximately 69 %.

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Evaluation and Transferability of the Noah Land Surface Model in Semiarid Environments

Journal of Hydrometeorology ◽

10.1175/jhm-402.1 ◽

2005 ◽

Vol 6 (1) ◽

pp. 68-84 ◽

Cited By ~ 97

Author(s):

Terri S. Hogue ◽

Luis Bastidas ◽

Hoshin Gupta ◽

Soroosh Sorooshian ◽

Ken Mitchell ◽

...

Keyword(s):

Land Surface ◽

Model Development ◽

Model Performance ◽

Land Surface Model ◽

Time Frame ◽

Climatic Conditions ◽

Systematic Evaluation ◽

Surface Model ◽

Monsoon Period ◽

Noah Land Surface Model

Abstract This paper investigates the performance of the National Centers for Environmental Prediction (NCEP) Noah land surface model at two semiarid sites in southern Arizona. The goal is to evaluate the transferability of calibrated parameters (i.e., direct application of a parameter set to a “similar” site) between the sites and to analyze model performance under the various climatic conditions that can occur in this region. A multicriteria, systematic evaluation scheme is developed to meet these goals. Results indicate that the Noah model is able to simulate sensible heat, ground heat, and ground temperature observations with a high degree of accuracy, using the optimized parameter sets. However, there is a large influx of moist air into Arizona during the monsoon period, and significant latent heat flux errors are observed in model simulations during these periods. The use of proxy site parameters (transferred parameter set), as well as traditional default parameters, results in diminished model performance when compared to a set of parameters calibrated specifically to the flux sites. Also, using a parameter set obtained from a longer-time-frame calibration (i.e., a 4-yr period) results in decreased model performance during nonstationary, short-term climatic events, such as a monsoon or El Niño. Although these results are specific to the sites in Arizona, it is hypothesized that these results may hold true for other case studies. In general, there is still the opportunity for improvement in the representation of physical processes in land surface models for semiarid regions. The hope is that rigorous model evaluation, such as that put forth in this analysis, and studies such as the Project for the Intercomparison of Land-Surface Processes (PILPS) San Pedro–Sevilleta, will lead to advances in model development, as well as parameter estimation and transferability, for use in long-term climate and regional environmental studies.

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Representation of vegetation effects on the snow-covered albedo in the Noah land surface model with multiple physics options

Geoscientific Model Development Discussions ◽

10.5194/gmdd-8-3197-2015 ◽

2015 ◽

Vol 8 (4) ◽

pp. 3197-3218

Author(s):

S. Park ◽

S. K. Park

Keyword(s):

East Asia ◽

Land Surface ◽

Surface Albedo ◽

Land Surface Model ◽

Surface Model ◽

Vegetation Types ◽

Area Index ◽

Snow Albedo ◽

Noah Land Surface Model ◽

Vegetation Effect

Abstract. Snow albedo plays a critical role in calculating the energy budget, but parameterization of the snow surface albedo is still under great uncertainty. It varies with snow grain size, snow cover thickness, snow age, forest shading factor and other variables. Snow albedo of forest is typically lower than that of short vegetation; thus snow albedo is dependent on the spatial distributions of characteristic land cover and on the canopy density and structure. In the Noah land surface model with multiple physics options (Noah-MP), almost all vegetation types in East Asia during winter have the minimum values of leaf area index (LAI) and stem area index (SAI), which are too low and do not consider the vegetation types. Because LAI and SAI are represented in terms of photosynthetic activeness, the vegetation effect rarely exerts on the surface albedo in winter in East Asia with only these parameters. Thus, we investigated the vegetation effects on the snow-covered albedo from observations and evaluated the model improvement by considering such effect. We found that calculation of albedo without proper reflection of the vegetation effect is mainly responsible for the large positive bias in winter. Therefore, we developed new parameters, called leaf index (LI) and stem index (SI), which properly manage the effect of vegetation structure on the winter albedo. As a result, the Noah-MP's performance in albedo has been significantly improved – RMSE is reduced by approximately 73%.

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