Analysis of WRF Model Performance over Subtropical Region of Delhi, India

Model performance and sensitivity to model physics options are studied with the Weather Research and Forecasting model (version 3.1.1) over Delhi region in India for surface and upper air meteorological parameters in summer and winter seasons. A case study with the model has been performed with different configurations, and the best physics options suited for this region have been, determined. Comparison between estimated and observed data was carried out through standard statistical measures. Generally, the combination of Pleim-Xiu land surface model, Pleim surface layer scheme, and Asymmetric Convective Model has been found to produce better estimates of temperature and relative humidity for Delhi region. Wind speed and direction estimations were observed best for MM5 similarity surface layer along with Yonsei University boundary layer scheme. Nested domains with higher resolutions were not helpful in improving the simulation results as per the current availability of the data. Overall, the present case study shows that the model has performed reasonably well over the subtropical region of Delhi.

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Assessment of WRF Land Surface Model Performance over West Africa

Advances in Meteorology ◽

10.1155/2020/6205308 ◽

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.

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Performance evaluation of the WRF model in a tropical region: wind speed analysis at different sites

10.20937/atm.52968 ◽

2021 ◽

Author(s):

Noéle Bissoli Perini Souza ◽

Erick Giovani Sperandio Nascimento ◽

Davidson Martins Moreira

Keyword(s):

Wind Speed ◽

Land Surface ◽

Wrf Model ◽

Land Surface Model ◽

Weather Research And Forecasting ◽

Tropical Region ◽

Surface Model ◽

Dry And Rainy Seasons

In this study, the performance of the mesoscale Weather Research and Forecasting (WRF) model is evaluated using combinations of three Planetary Boundary Layer (PBL) and three Land Surface Model (LSM) schemes, in order to identify the optimal parameters for the determination of wind speed in a tropical region. The state of Bahia in Brazil is selected as the location for the case study and simulations are performed over a period of eight months between 2015 and 2016. This is done to ensure that the dry and rainy seasons at the three different experimental sites—Esplanada, Mucuri, and Mucugê—are well separated from each other. The results of the simulations are compared with the observational data obtained from three towers equipped with anemometers at heights of 80, 100, 120 and 150 m, strategically placed at each site. Overestimation of wind speed is observed in the simulations, despite similarities between the simulated and observed wind directions. In addition, the accuracies of simulations corresponding to sites that are closer to the ocean are observed to be lower—the most accurate wind speed estimates are obtained corresponding to Mucugê, which is located farthest from the ocean. Finally, analysis of the results obtained from each tower accounting for periods with higher and lower precipitation reveals that the combination of the PBL-YSU scheme with the LSM-RUC scheme yields the best results.

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WRF Model Sensitivity to Choice of Parameterization over South America: Validation against Surface Variables

Monthly Weather Review ◽

10.1175/2010mwr3358.1 ◽

2010 ◽

Vol 138 (8) ◽

pp. 3342-3355 ◽

Cited By ~ 74

Author(s):

Juan J. Ruiz ◽

Celeste Saulo ◽

Julia Nogués-Paegle

Keyword(s):

Soil Moisture ◽

South America ◽

Land Surface ◽

Surface Wind ◽

Model Performance ◽

Wrf Model ◽

Land Surface Model ◽

Forecast Model ◽

South Atlantic Convergence Zone ◽

Surface Model

Abstract The Weather and Research Forecast model is tested over South America in different configurations to identify the one that gives the best estimates of observed surface variables. Systematic, nonsystematic, and total errors are computed for 48-h forecasts initialized with the NCEP Global Data Assimilation System (GDAS). There is no unique model design that best fits all variables over the whole domain, and nonsystematic errors for all configurations differ little from one another; such differences are in most cases smaller than the observed day-to-day variability. An ensemble mean consisting of runs with different parameterizations gives the best skill for the whole domain. Surface variables are highly sensitive to the choice of land surface models. Surface temperature is well represented by the Noah land model, but dewpoint temperature is best estimated by the simplest land surface model considered here, which specifies soil moisture based on climatology. This underlines the need for better understanding of humid processes at the subgrid scale. Surface wind errors decrease the intensity of the low-level jet, reducing expected heat and moisture advection over southeast South America (SESA), with negative precipitation errors over SESA and positive biases over the South Atlantic convergence zone (SACZ). This pattern of errors suggests feedbacks between wind errors, precipitation, and surface processes as follows: an increase of precipitation over the SACZ produces compensating descent in SESA, with more stable stratification, less rain, less soil moisture, and decreased rain. This is a clear example of how local errors are related to regional circulation, and suggests that improvement of model performance requires not only better parameterizations at the subgrid scales, but also improved regional models.

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WRF Sensitivity Analysis in Wind and Temperature Fields Simulation for the Northern Sahara and the Mediterranean Basin

Atmosphere ◽

10.3390/atmos11030259 ◽

2020 ◽

Vol 11 (3) ◽

pp. 259 ◽

Cited By ~ 1

Author(s):

Umberto Rizza ◽

Enrico Mancinelli ◽

Elisa Canepa ◽

Jacques Piazzola ◽

Tathy Missamou ◽

...

Keyword(s):

Surface Layer ◽

Wind Speed ◽

Land Surface ◽

Mediterranean Basin ◽

Wrf Model ◽

Land Surface Model ◽

Temperature Fields ◽

Surface Model ◽

The Mediterranean ◽

The Mediterranean Basin

Different configurations for the Weather Research and Forecasting (WRF) model were evaluated to improve wind and temperature fields predictions in the Northern Sahara and the Mediterranean basin. Eight setups, associated with different combinations of the surface layer physical parameters, the land surface model, and the grid nudging parameters, were considered. Numerical simulations covered the entire month of November 2017. Model results were compared with surface data from meteorological stations. The introduction of the grid nudging parameters leads to a general improvement of the modeled 10 m wind speed and 2 m temperature. In particular, nudging of wind speed parameter inside the planetary boundary layer (PBL) provides the most remarkable differences. In contrast, the nudging of temperature and relative humidity parameters inside the PBL may be switched off to reduce computational time and data storage. Furthermore, it was shown that the prediction of the 10 m wind speed and 2 m temperature is quite sensitive to the choice of the surface layer scheme and the land surface model. This paper provides useful suggestions to improve the setup of the WRF model in the Northern Sahara and the Mediterranean basin. These results are also relevant for topics related with the emission of mineral dust and sea spray within the Mediterranean region.

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Can a physically-based land surface model accurately represent evapotranspiration partitioning? A case study in a humid boreal forest

Agricultural and Forest Meteorology ◽

10.1016/j.agrformet.2021.108410 ◽

2021 ◽

Vol 304-305 ◽

pp. 108410

Author(s):

Bram Hadiwijaya ◽

Pierre-Erik Isabelle ◽

Daniel F. Nadeau ◽

Steeve Pepin

Keyword(s):

Boreal Forest ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Physically Based ◽

Evapotranspiration Partitioning

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Benchmarking and Process Diagnostics of Land Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-17-0209.1 ◽

2018 ◽

Vol 19 (11) ◽

pp. 1835-1852 ◽

Cited By ~ 13

Author(s):

Grey S. Nearing ◽

Benjamin L. Ruddell ◽

Martyn P. Clark ◽

Bart Nijssen ◽

Christa Peters-Lidard

Keyword(s):

Land Surface ◽

Model Performance ◽

Land Surface Model ◽

Surface Model ◽

Process Diagnostics ◽

Surface Energy Fluxes ◽

Structural Error ◽

Intercomparison Project ◽

Parameter Values ◽

Process Level

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

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Development and application of the WRFPLUS-Chem online chemistry adjoint and WRFDA-Chem assimilation system

Geoscientific Model Development ◽

10.5194/gmd-8-1857-2015 ◽

2015 ◽

Vol 8 (6) ◽

pp. 1857-1876 ◽

Cited By ~ 11

Author(s):

J. J. Guerrette ◽

D. K. Henze

Keyword(s):

Land Surface ◽

Vertical Mixing ◽

Model Performance ◽

Land Surface Model ◽

Sensitivity Study ◽

Climate Impacts ◽

Surface Model ◽

Time Period ◽

Develop Land ◽

The Impact

Abstract. Here we present the online meteorology and chemistry adjoint and tangent linear model, WRFPLUS-Chem (Weather Research and Forecasting plus chemistry), which incorporates modules to treat boundary layer mixing, emission, aging, dry deposition, and advection of black carbon aerosol. We also develop land surface and surface layer adjoints to account for coupling between radiation and vertical mixing. Model performance is verified against finite difference derivative approximations. A second-order checkpointing scheme is created to reduce computational costs and enable simulations longer than 6 h. The adjoint is coupled to WRFDA-Chem, in order to conduct a sensitivity study of anthropogenic and biomass burning sources throughout California during the 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. A cost-function weighting scheme was devised to reduce the impact of statistically insignificant residual errors in future inverse modeling studies. Results of the sensitivity study show that, for this domain and time period, anthropogenic emissions are overpredicted, while wildfire emission error signs vary spatially. We consider the diurnal variation in emission sensitivities to determine at what time sources should be scaled up or down. Also, adjoint sensitivities for two choices of land surface model (LSM) indicate that emission inversion results would be sensitive to forward model configuration. The tools described here are the first step in conducting four-dimensional variational data assimilation in a coupled meteorology–chemistry model, which will potentially provide new constraints on aerosol precursor emissions and their distributions. Such analyses will be invaluable to assessments of particulate matter health and climate impacts.

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Using Cosmic-Ray Neutron Probes in Validating Satellite Soil Moisture Products and Land Surface Models

Water ◽

10.3390/w11071362 ◽

2019 ◽

Vol 11 (7) ◽

pp. 1362 ◽

Cited By ~ 2

Author(s):

Mustafa Berk Duygu ◽

Zuhal Akyürek

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Land Surface Model ◽

Cosmic Ray ◽

Surface Model ◽

High Temporal Resolution ◽

Land Surface Models ◽

Statistical Measures ◽

Surface Models ◽

Global Land

Soil moisture content is one of the most important parameters of hydrological studies. Cosmic-ray neutron sensing is a promising proximal soil moisture sensing technique at intermediate scale and high temporal resolution. In this study, we validate satellite soil moisture products for the period of March 2015 and December 2018 by using several existing Cosmic Ray Neutron Probe (CRNP) stations of the COSMOS database and a CRNP station that was installed in the south part of Turkey in October 2016. Soil moisture values, which were inferred from the CRNP station in Turkey, are also validated using a time domain reflectometer (TDR) installed at the same location and soil water content values obtained from a land surface model (Noah LSM) at various depths (0.1 m, 0.3 m, 0.6 m and 1.0 m). The CRNP has a very good correlation with TDR where both measurements show consistent changes in soil moisture due to storm events. Satellite soil moisture products obtained from the Soil Moisture and Ocean Salinity (SMOS), the METOP-A/B Advanced Scatterometer (ASCAT), Soil Moisture Active Passive (SMAP), Advanced Microwave Scanning Radiometer 2 (AMSR2), Climate Change Initiative (CCI) and a global land surface model Global Land Data Assimilation System (GLDAS) are compared with the soil moisture values obtained from CRNP stations. Coefficient of determination ( r 2 ) and unbiased root mean square error (ubRMSE) are used as the statistical measures. Triple Collocation (TC) was also performed by considering soil moisture values obtained from different soil moisture products and the CRNPs. The validation results are mainly influenced by the location of the sensor and the soil moisture retrieval algorithm of satellite products. The SMAP surface product produces the highest correlations and lowest errors especially in semi-arid areas whereas the ASCAT product provides better results in vegetated areas. Both global and local land surface models’ outputs are highly compatible with the CRNP soil moisture values.

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

Hydrology Research ◽

10.2166/nh.2011.071 ◽

2011 ◽

Vol 42 (2-3) ◽

pp. 95-112 ◽

Cited By ~ 31

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.

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