scholarly journals Evaluating Multiple WRF Configurations and Forcing over the Northern Patagonian Icecap (NPI) and Baker River Basin

Atmosphere ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 815
Author(s):  
Marcelo Somos-Valenzuela ◽  
Francisco Manquehual-Cheuque
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Land Surface Model ◽  
Short Wave ◽  
Reanalysis Data ◽  
Wave Radiation ◽  
Surface Model ◽  
Nwp Model

The use of numerical weather prediction (NWP) model to dynamically downscale coarse climate reanalysis data allows for the capture of processes that are influenced by land cover and topographic features. Climate reanalysis downscaling is useful for hydrology modeling, where catchment processes happen on a spatial scale that is not represented in reanalysis models. Selecting proper parameterization in the NWP for downscaling is crucial to downscale the climate variables of interest. In this work, we are interested in identifying at least one combination of physics in the Weather Research Forecast (WRF) model that performs well in our area of study that covers the Baker River Basin and the Northern Patagonian Icecap (NPI) in the south of Chile. We used ERA-Interim reanalysis data to run WRF in twenty-four different combinations of physics for three years in a nested domain of 22.5 and 4.5 km with 34 vertical levels. From more to less confident, we found that, for the planetary boundary layer (PBL), the best option is to use YSU; for the land surface model (LSM), the best option is the five-Layer Thermal, RRTM for longwave, Dudhia for short wave radiation, and Thompson for the microphysics. In general, the model did well for temperature (average, minimum, maximum) for most of the observation points and configurations. Precipitation was good, but just a few configurations stood out (i.e., conf-9 and conf-10). Surface pressure and Relative Humidity results were not good or bad, and it depends on the statistics with which we evaluate the time series (i.e., KGE or NSE). The results for wind speed were inferior; there was a warm bias in all of the stations. Once we identify the best configuration in our experiment, we run WRF for one year using ERA5 and FNL0832 climate reanalysis. Our results indicate that Era-interim provided better results for precipitation. In the case of temperature, FNL0832 gave better results; however, all of the models’ performances were good. Therefore, working with ERA-Interim seems the best option in this region with the physics selected. We did not experiment with changes in resolution, which may have improved results with ERA5 that has a better spatial and temporal resolution.

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.

A novel application of an adaptive surrogate-based modeling optimization (ASMO) for the Community Territory System Model (CTSM) in Alaska

2021 ◽  
Author(s):  
Yifan Cheng ◽  
Andrew Newman ◽  
Sean Swenson ◽  
David Lawrence ◽  
Anthony Craig ◽  
...  
Keyword(s):  
High Resolution ◽  
River Basin ◽  
Land Surface ◽  
Computational Cost ◽  
Reanalysis Data ◽  
Freshwater Ecosystems ◽  
System Model ◽  
Surface Model ◽  
Yukon River ◽  
Yukon River Basin

<p>Climate-induced changes in snow cover, river flow, and freshwater ecosystems will greatly affect the indigenous groups in the Alaska and Yukon River Basin. To support policy-making on climate adaptation and mitigation for these underrepresented groups, an ongoing interdisciplinary effort is being made to combine Indigenous Knowledge with western science (https://www.colorado.edu/research/arctic-rivers/).</p><p>A foundational component of this project is a high fidelity representation of the aforementioned land surface processes. To this end, we aim to obtain a set of reliable high-resolution parameters for the Community Territory System Model (CTSM) for the continental scale domain of Alaska and the entire Yukon River Basin, which will be used in climate change simulations. CTSM is a complex, physically based state-of-the-science land surface model that includes complex vegetation and canopy representation, a multi-layer snow model, as well as hydrology and frozen soil physics necessary for the representation of streamflow and permafrost. Two modifications to the default CTSM configuration were made. First, we used CTSM that is implemented with hillslope hydrology to better capture the fine-scale hydrologic spatial heterogeneity in complex terrain. Second, we updated the input soil textures and organic carbon in CTSM using the high-resolution SoilGrid dataset.</p><p>In this study, we performed a multi-objective optimization on snow and streamflow metrics using an adaptive surrogate-based modeling optimization (ASMO). ASMO permits optimization of complex land-surface models over large domains through the use of surrogate models to minimize the computational cost of running the full model for every parameter combination. We ran CTSM at a spatial resolution of 1/24<sup>th</sup> degree and a temporal resolution of one hour using the ERA5 reanalysis data as the meteorological forcings. The ERA5 reanalysis data were bias-corrected to account for the orographic effects. We will discuss the ASMO-CTSM coupling workflow, performance characteristics of the optimization (e.g., computational cost, iterations), and comparisons of the default configuration and optimized model performance.</p>

scholarly journals The Met Office Unified Model Global Atmosphere 4.0 and JULES Global Land 4.0 configurations

2014 ◽  
Vol 7 (1) ◽  
pp. 361-386 ◽  
Author(s):  
D. N. Walters ◽  
K. D. Williams ◽  
I. A. Boutle ◽  
A. C. Bushell ◽  
J. M. Edwards ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Regional Climate ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Development Cycle ◽  
Global Land ◽  
Overall Performance ◽  
Ongoing Development

Abstract. We describe Global Atmosphere 4.0 (GA4.0) and Global Land 4.0 (GL4.0): configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) community land surface model developed for use in global and regional climate research and weather prediction activities. GA4.0 and GL4.0 are based on the previous GA3.0 and GL3.0 configurations, with the inclusion of developments made by the Met Office and its collaborators during its annual development cycle. This paper provides a comprehensive technical and scientific description of GA4.0 and GL4.0 as well as details of how these differ from their predecessors. We also present the results of some initial evaluations of their performance. Overall, performance is comparable with that of GA3.0/GL3.0; the updated configurations include improvements to the science of several parametrisation schemes, however, and will form a baseline for further ongoing development.

Estimates of the Earth surface influence on the accuracy of numerical prediction of air temperature in Belarus using the WRF model

2021 ◽  
Vol 4 ◽  
pp. 50-68
Author(s):  
S.А. Lysenko ◽  
◽  
P.О. Zaiko ◽  
Keyword(s):  
Land Use ◽  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Land Surface ◽  
Lead Time ◽  
Weather Prediction ◽  
Wrf Model ◽  
Surface Model ◽  
Numerical Weather ◽  
Global Land

The spatial structure of land use and biophysical characteristics of land surface (albedo, leaf index, and vegetation cover) are updated using the GLASS (Global Land Surface Satellite) and GLC2019 (Global Land Cover, 2019) modern satellite databases for mesoscale numerical weather prediction with the WRF model for the territory of Belarus. The series of WRF-based numerical experiments was performed to verify the influence of the updated characteristics on the forecast quality for some difficult to predict winter cases. The model was initialized by the GFS (Global Forecast System, NCEP) global numerical weather prediction model. It is shown that the use of high-resolution land use data in the WRF and the consideration of the new albedo and leaf index distribution over the territory of Belarus can reduce the root-mean-square error (RMSE) of short-range (to 48 hours) forecasts of surface air temperature by 16–33% as compared to the GFS. The RMSE of the temperature forecast for the weather stations in Belarus for a forecast lead time of 12, 24, 36, and 48 hours decreased on average by 0.40°С (19%), 0.35°С (10%), 0.68°С (23%), and 0.56°С (15%), respectively. The most significant decrease in RMSE of the numerical forecast of temperature (up to 2.1 °С) was obtained for the daytime (for a lead time of 12 and 36 hours), when positive feedbacks between albedo and temperature of the land surface are manifested most. Keywords: numerical weather prediction, WRF, digital land surface model, albedo, leaf area index, forecast model validation

The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing

2011 ◽  
Vol 42 (2-3) ◽  
pp. 95-112 ◽  
Author(s):  
Venkat Lakshmi ◽  
Seungbum Hong ◽  
Eric E. Small ◽  
Fei Chen
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Processes ◽  
Surface Model ◽  
Land Surface Processes ◽  
Area Index ◽  
Vegetation Fraction ◽  
Moisture Variation

The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), surface temperature (TSK), and vegetation water content (VegWC) derived from satellite sensors such as Moderate Resolution Imaging Spectroradiometer (MODIS) and EOS Advanced Microwave Scanning Radiometer (AMSR-E). The study provides general information about a physiological behavior of vegetation for various environmental conditions. Second, using a coupled mesoscale/land surface model, we examine the effects of vegetation and its relationship with soil moisture on the simulated land–atmospheric interactions through the model sensitivity tests. The Weather Research and Forecasting (WRF) model was selected for this study, and the Noah land surface model (Noah LSM) implemented in the WRF model was used for the model coupled system. This coupled model was tested through two parameterization methods for vegetation fraction using MODIS data and through model initialization of soil moisture from High Resolution Land Data Assimilation System (HRLDAS). Finally, this study evaluates the model improvements for each simulation method.

scholarly journals Impacts of land surface model and land use data on WRF model simulations of rainfall and temperature over Lake Tana Basin, Ethiopia

Heliyon ◽  
2019 ◽  
Vol 5 (9) ◽  
pp. e02469 ◽  
Author(s):  
Achenafi Teklay ◽  
Yihun T. Dile ◽  
Dereje H. Asfaw ◽  
Haimanote K. Bayabil ◽  
Kibruyesfa Sisay
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Lake Tana ◽  
Model Simulations ◽  
Lake Tana Basin

scholarly journals Assessment of WRF Land Surface Model Performance over West Africa

2020 ◽  
Vol 2020 ◽  
pp. 1-30
Author(s):  
Ifeanyi C. Achugbu ◽  
Jimy Dudhia ◽  
Ayorinde A. Olufayo ◽  
Ifeoluwa A. Balogun ◽  
Elijah A. Adefisan ◽  
...  
Keyword(s):  
West Africa ◽  
Land Surface ◽  
Model Performance ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Reanalysis Dataset ◽  
The North ◽  
Temperature And Precipitation ◽  
Moisture Advection

Simulations with four land surface models (LSMs) (i.e., Noah, Noah-MP, Noah-MP with ground water GW option, and CLM4) using the Weather Research and Forecasting (WRF) model at 12 km horizontal grid resolution were carried out as two sets for 3 months (December–February 2011/2012 and July–September 2012) over West Africa. The objective is to assess the performance of WRF LSMs in simulating meteorological parameters over West Africa. The model precipitation was assessed against TRMM while surface temperature was compared with the ERA-Interim reanalysis dataset. Results show that the LSMs performed differently for different variables in different land-surface conditions. Based on precipitation and temperature, Noah-MP GW is overall the best for all the variables and seasons in combination, while Noah came last. Specifically, Noah-MP GW performed best for JAS temperature and precipitation; CLM4 was the best in simulating DJF precipitation, while Noah was the best in simulating DJF temperature. Noah-MP GW has the wettest Sahel while Noah has the driest one. The strength of the Tropical Easterly Jet (TEJ) is strongest in Noah-MP GW and Noah-MP compared with that in CLM4 and Noah. The core of the African Easterly Jet (AEJ) lies around 12°N in Noah and 15°N for Noah-MP GW. Noah-MP GW and Noah-MP simulations have stronger influx of moisture advection from the southwesterly monsoonal wind than the CLM4 and Noah with Noah showing the least influx. Also, analysis of the evaporative fraction shows sharp gradient for Noah-MP GW and Noah-MP with wetter Sahel further to the north and further to the south for Noah. Noah-MP-GW has the highest amount of soil moisture, while the CLM4 has the least for both the JAS and DJF seasons. The CLM4 has the highest LH for both DJF and JAS seasons but however has the least SH for both DJF and JAS seasons. The principal difference between the LSMs is in the vegetation representation, description, and parameterization of the soil water column; hence, improvement is recommended in this regard.

scholarly journals The Met Office Unified Model Global Atmosphere 6.0/6.1 and JULES Global Land 6.0/6.1 configurations

2017 ◽  
Vol 10 (4) ◽  
pp. 1487-1520 ◽  
Author(s):  
David Walters ◽  
Ian Boutle ◽  
Malcolm Brooks ◽  
Thomas Melvin ◽  
Rachel Stratton ◽  
...  
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Model Performance ◽  
Land Surface Model ◽  
Unified Model ◽  
Surface Model ◽  
Main Trunk ◽  
Global Land ◽  
Prediction Systems ◽  
The Tropics

Abstract. We describe Global Atmosphere 6.0 and Global Land 6.0 (GA6.0/GL6.0): the latest science configurations of the Met Office Unified Model and JULES (Joint UK Land Environment Simulator) land surface model developed for use across all timescales. Global Atmosphere 6.0 includes the ENDGame (Even Newer Dynamics for General atmospheric modelling of the environment) dynamical core, which significantly increases mid-latitude variability improving a known model bias. Alongside developments of the model's physical parametrisations, ENDGame also increases variability in the tropics, which leads to an improved representation of tropical cyclones and other tropical phenomena. Further developments of the atmospheric and land surface parametrisations improve other aspects of model performance, including the forecasting of surface weather phenomena. We also describe GA6.1/GL6.1, which includes a small number of long-standing differences from our main trunk configurations that we continue to require for operational global weather prediction. Since July 2014, GA6.1/GL6.1 has been used by the Met Office for operational global numerical weather prediction, whilst GA6.0/GL6.0 was implemented in its remaining global prediction systems over the following year.

scholarly journals River basin hydrology in a global off-line land-surface model

1999 ◽  
Vol 104 (D16) ◽  
pp. 19661-19673 ◽  
Author(s):  
Michael G. Bosilovich ◽  
Runhua Yang ◽  
Paul R. Houser
Keyword(s):  
River Basin ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Basin Hydrology
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