Configuration and Evaluation of the WRF Model for the Study of Hawaiian Regional Climate

2012 ◽  
Vol 140 (10) ◽  
pp. 3259-3277 ◽  
Author(s):  
Chunxi Zhang ◽  
Yuqing Wang ◽  
Axel Lauer ◽  
Kevin Hamilton
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Model Simulation ◽  
Regional Climate ◽  
Hawaiian Islands ◽  
Wrf Model ◽  
Trade Wind ◽  
Preliminary Evaluation ◽  
The Mean

Abstract The Weather Research and Forecasting (WRF) model V3.3 has been configured for the Hawaiian Islands as a regional climate model for the region (HRCM). This paper documents the model configuration and presents a preliminary evaluation based on a continuous 1-yr simulation forced by observed boundary conditions with 3-km horizontal grid spacing in the inner nested domain. The simulated vertical structure of the temperature and humidity are compared with twice-daily radiosonde observations at two stations. Generally the trade wind inversion (TWI) height and occurrence days are well represented. The simulation over the islands is compared with observations from nine surface climatological stations and a dense network of precipitation stations. The model simulation has generally small biases in the simulated surface temperature, relative humidity, and wind speed. The model realistically simulated the magnitude and geographical distribution of the mean rainfall over the Hawaiian Islands. In addition, the model simulation reproduced reasonably well the individual heavy rainfall events as seen from the time series of pentad mean rainfall averaged over island scales. Also the model reproduced the geographical variation of the mean diurnal rainfall cycle even though the observed diurnal cycle displays quite different features over different islands. Comparison with results obtained using the land surface dataset from the official release of the WRF model confirmed that the newly implemented land surface dataset generally improved the simulation of surface variables. These results demonstrate that the WRF can be a useful tool for dynamical downscaling of regional climate over the Hawaiian Islands.

scholarly journals Streamflow Data from Small Basins: A Challenging Test to High-Resolution Regional Climate Modeling

2011 ◽  
Vol 12 (5) ◽  
pp. 900-912 ◽  
Author(s):  
Kerstin Stahl ◽  
Lena M. Tallaksen ◽  
Lukas Gudmundsson ◽  
Jens H. Christensen
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Temporal Dynamics ◽  
Model Simulation ◽  
Regional Climate ◽  
Regional Scale ◽  
Climate Simulation ◽  
Change Scenario

Abstract Land surface models and large-scale hydrological models provide the basis for studying impacts of climate and anthropogenic changes on continental- to regional-scale hydrology. Hence, there is a need for comparison and validation of simulated characteristics of spatial and temporal dynamics with independent observations. This study introduces a novel validation framework that relates to common hydrological design measures. The framework is tested by comparing anomalies of runoff from a high-resolution climate-model simulation for Europe with a large number of streamflow observations from small near-natural basins. The regional climate simulation was performed as a “poor man’s reanalysis,” involving a dynamical downscaling of the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) with the Danish “HIRHAM5” model. For 19 different anomaly levels, two indices evaluate the temporal agreement (i.e., the occurrence and frequency of dry and wet events based on daily anomalies), whereas two other indices compare the interannual variability and trends based on annual anomalies. Benchmarks on each index facilitated a comparison across indices, anomaly levels, and basins. The lowest agreement of observed and simulated anomalies was found for dry anomalies. Weak to moderately wet anomalies agreed best, but agreement dropped again for the wettest anomalies. The results could guide the decision on thresholds if this regional climate model were used for the assessment of climate change scenario impacts on flood and drought statistics. Indices vary across Europe, but a gradient with decreasing correspondence between observed and simulated runoff characteristics from west to east, from lower to higher elevations, and from fast to slowly responding basins can be distinguished. The suggested indices can easily be adapted to other study areas and model types to assist in assessing the reliability of predictions of hydrological change.

Hydrological evaluation of high-resolution precipitation estimates from the WRF model in the Third Pole river basins

2021 ◽  
Author(s):  
He Sun ◽  
Fengge Su ◽  
Zhihua He ◽  
Tinghai Ou ◽  
Deliang Chen ◽  
...  
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Hydrological Modeling ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Infiltration Capacity ◽  
Flood Prediction ◽  
Daily Streamflow ◽  
Precipitation Estimates

AbstractIn this study, two sets of precipitation estimates based on the regional Weather Research and Forecasting model (WRF) –the high Asia refined analysis (HAR) and outputs with a 9 km resolution from WRF (WRF-9km) are evaluated at both basin and point scales, and their potential hydrological utilities are investigated by driving the Variable Infiltration Capacity (VIC) large-scale land surface hydrological model in seven Third Pole (TP) basins. The regional climate model (RCM) tends to overestimate the gauge-based estimates by 20–95% in annual means among the selected basins. Relative to the gauge observations, the RCM precipitation estimates can accurately detect daily precipitation events of varying intensities (with absolute bias < 3 mm). The WRF-9km exhibits a high potential for hydrological application in the monsoon-dominated basins in the southeastern TP (with NSE of 0.7–0.9 and bias of -11% to 3%), while the HAR performs well in the upper Indus (UI) and upper Brahmaputra (UB) basins (with NSE of 0.6 and bias of -15% to -9%). Both the RCM precipitation estimates can accurately capture the magnitudes of low and moderate daily streamflow, but show limited capabilities in flood prediction in most of the TP basins. This study provides a comprehensive evaluation of the strength and limitation of RCMs precipitation in hydrological modeling in the TP with complex terrains and sparse gauge observations.

scholarly journals Precipitation Simulations Using WRF as a Nested Regional Climate Model

2009 ◽  
Vol 48 (10) ◽  
pp. 2152-2159 ◽  
Author(s):  
Melissa S. Bukovsky ◽  
David J. Karoly
Keyword(s):  
Regional Climate Model ◽  
Land Surface ◽  
Climate Model ◽  
Model Simulation ◽  
Regional Climate ◽  
Warm Season ◽  
Weather Forecast ◽  
Weather Research And Forecasting ◽  
Model Setup ◽  
Environmental Prediction

Abstract This note examines the sensitivity of simulated U.S. warm-season precipitation in the Weather Research and Forecasting model (WRF), used as a nested regional climate model, to variations in model setup. Numerous options have been tested and a few of the more interesting and unexpected sensitivities are documented here. Specifically, the impacts of changes in convective and land surface parameterizations, nest feedbacks, sea surface temperature, and WRF version on mean precipitation are evaluated in 4-month-long simulations. Running the model over an entire season has brought to light some issues that are not otherwise apparent in shorter, weather forecast–type simulations, emphasizing the need for careful scrutiny of output from any model simulation. After substantial testing, a reasonable model setup was found that produced a definite improvement in the climatological characteristics of precipitation over that from the National Centers for Environmental Prediction–National Center for Atmospheric Research global reanalysis, the dataset used for WRF initial and boundary conditions in this analysis.

scholarly journals Land surface model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v3.9 model over North America

2020 ◽  
Vol 13 (11) ◽  
pp. 5345-5366
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature And Precipitation ◽  
Model Ensembles ◽  
Precipitation Events

Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we analyze land–atmosphere interactions within four simulations performed by the WRF model from 1980 to 2012 over North America, using three different LSMs. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Our results show that the WRF simulation of the climatology of heat extremes can be 5 ∘C warmer and 6 d longer depending on the employed LSM component, and similarly for cold extremes and heavy precipitation events. Areas showing large uncertainty in WRF-simulated extreme events are also identified in a model ensemble from three different regional climate model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. Thus, studies based on multi-model ensembles and reanalyses should include a variety of LSM configurations to account for the uncertainty arising from this model component or to test the performance of the selected LSM component before running the whole simulation. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating extreme temperature and precipitation events, which in turn will help to reduce uncertainties in climate model projections.

scholarly journals Land Surface Model influence on the simulated climatologies of temperature and precipitation extremes in the WRF v.3.9 model over North America

2020 ◽  
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
North America ◽  
Regional Climate Model ◽  
Extreme Events ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Temperature And Precipitation

Abstract. The representation and projection of extreme temperature and precipitation events in regional and global climate models are of major importance for the study of climate change impacts. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensity, duration and frequency of these extremes. Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) model to determine the influence of the land surface model (LSM) component on uncertainties associated with extreme events. First, we evaluate land-atmosphere interactions within four simulations performed by the WRF model using three different LSMs from 1980 to 2012 over North America. Results show LSM-dependent differences at regional scales in the frequency of occurrence of events when surface conditions are altered by atmospheric forcing or land processes. The inter-model range of extreme statistics across the WRF simulations is large, particularly for indices related to the intensity and duration of temperature and precipitation extremes. Areas showing large uncertainty in WRF simulated extreme events are also identified in a model ensemble from three different Regional Climate Model (RCM) simulations participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project, revealing the implications of these results for other model ensembles. This study illustrates the importance of the LSM choice in climate simulations, supporting the development of new modeling studies using different LSM components to understand inter-model differences in simulating temperature and precipitation extreme events, which in turn will help to reduce uncertainties in climate model projections.

scholarly journals Downscaling of Climate Change in the Hawaii Region Using CMIP5 Results: On the Choice of the Forcing Fields*

2013 ◽  
Vol 26 (24) ◽  
pp. 10006-10030 ◽  
Author(s):  
Axel Lauer ◽  
Chunxi Zhang ◽  
Oliver Elison-Timm ◽  
Yuqing Wang ◽  
Kevin Hamilton
Keyword(s):  
Wind Speed ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Wrf Model ◽  
Cmip5 Models ◽  
Trade Wind ◽  
Cmip5 Model ◽  
Model Experiments ◽  
Trade Wind Inversion ◽  
The Individual

Abstract The Weather Research and Forecasting (WRF) model has been configured as a regional climate model for the Hawaii region (HRCM) to assess the uncertainties associated with the pseudo–global warming (PGW) downscaling method using different warming increments from phase 5 of the Coupled Model Intercomparison Project (CMIP5) model experiments. Results from 15-km downscaling experiments using warming increments from 10 individual CMIP5 models for the two warming scenarios representative concentration pathway 4.5 (RCP4.5) and 8.5 (RCP8.5) are compared with experiments using multimodel mean warming increments. The results show that changes in 2-m temperatures, 10-m wind speed, rainfall, water vapor path, and trade wind inversion vary significantly among the individual model experiments. This translates into large uncertainties when picking one particular CMIP5 model to provide the warming increments for dynamical downscaling in the Hawaii region. The simulations also show that, despite the large interexperiment spread, a single downscaling experiment using multimodel mean warming increments gives very similar results to the ensemble mean of downscaling experiments using warming increments obtained from 10 individual CMIP5 models. Robust changes of the projected climate by the end of the twenty-first century in the Hawaii region shown by most downscaling experiments include increasing 2-m temperatures with stronger warming at higher elevations, a large increase in precipitable water, and an increase in the number of days with a trade wind inversion (TWI). Furthermore, most experiments agree on a reduction in TWI height and an increase in the TWI strength. Uncertainties in the projected changes in rainfall and 10-m wind speed are large and there is little consensus among the individual downscaling experiments.

Land Surface Model influence on the simulated climatologies of extreme temperature and precipitation events within the WRF model over North America

2020 ◽  
Author(s):  
Almudena García-García ◽  
Francisco José Cuesta-Valero ◽  
Hugo Beltrami ◽  
J. Fidel González-Rouco ◽  
Elena García-Bustamante ◽  
...  
Keyword(s):  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Extreme Temperature ◽  
Wrf Model ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Simulations ◽  
Temperature And Precipitation ◽  
Precipitation Events

&lt;p class=&quot;western&quot;&gt;&lt;span&gt;The representation and projection of extreme temperature and precipitation events in climate models are of major importance for developing polices to build communities&amp;#8217; resilience in the face of climate change. However, state-of-the-art global and regional climate model simulations yield a broad inter-model range of intensities, durations and frequencies of these extremes. &lt;/span&gt;&lt;/p&gt; &lt;p class=&quot;western&quot;&gt;&lt;span&gt;Here, we present a modeling experiment using the Weather Research and Forecasting (WRF) Regional Climate Model (RCM) to determine the influence of the choice of land surface model (LSM) component on the uncertainty in the simulation of extreme event statistics. First, we evaluate land-atmosphere interactions within four simulations performed with the WRF model coupled to three different LSMs from 1980 to 2012 over North America. Results show regional differences among simulations for the frequency of events when surface conditions are altered by atmospheric forcing or by land surface processes. Second, we find a large inter-model range of extreme statistics across the ensemble of WRF-LSM simulations. This is particularly the case for indices related to the intensity and duration of temperature and precipitation extremes. &lt;/span&gt;&lt;/p&gt; &lt;p class=&quot;western&quot;&gt;&lt;span&gt;Regions displaying large uncertainty in the WRF simulation of extreme events are also identified in a model ensemble experiment carried out with three different RCMs participating in the Coordinated Regional Climate Downscaling Experiment (CORDEX) project. This agreement between the model simulations performed in this work and the set of CORDEX simulations suggests that the implications of our results are valid for other model ensembles. This study illustrates the importance of supporting the development of new multi-LSM modeling studies to understand inter-model differences in simulating extreme events, ultimately helping to narrow down the range across climate model projections.&lt;/span&gt;&lt;/p&gt;

Varying horizontal resolution and land surface schemes in soil moisture – air temperature coupling, calculated with the WRF model for the MENA-CORDEX domain

2021 ◽  
Author(s):  
Katiana Constantinidou ◽  
Panos Hadjinicolaou ◽  
George Zittis ◽  
Jos Lelieveld
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Wrf Model ◽  
Correlation Coefficients ◽  
Horizontal Resolution ◽  
Western Asia ◽  
June July

&lt;p&gt;We study the effect of increased resolution and more elaborate representation of land surface on the soil moisture &amp;#8211; air temperature coupling with the WRF climate downscaling model. Previous work indicated reduced winter/spring rainfall and enhanced summer heat. Two different land surface schemes (LSS) Noah and NoahMP with dynamic vegetation option turned on are incorporated in the WRF regional climate model in simulations at 50 and 16 km horizontal resolution over the region of Middle East and North Africa (MENA) for the period of 2000-2004. An analysis is performed for the summer season (June-July-August; JJA) for the four-year period, employing coupling metrics, i.e. associations between climatic variables related to the soil moisture &amp;#8211; air temperature coupling. We calculate correlation coefficients between time-series consisting of 10-day averages, non-overlapping, for related surface climate variables from the WRF simulations and observational datasets. This assessment indicates that the NoahMP scheme simulates a stronger coupling than the Noah, irrespective of the resolution. The strength of this coupling varies at different areas around the MENA when considering mean or maximum 2-meter air temperature, with the NoahMP at 16-km producing the strongest effect over the western Asia part of the domain.&lt;/p&gt;

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