Search Results

You are looking at 41 - 50 of 60 items for

  • Author or Editor: Jimy Dudhia x
  • Refine by Access: All Content x
Clear All Modify Search
Daniel Argüeso
,
José M. Hidalgo-Muñoz
,
Sonia R. Gámiz-Fortis
,
María Jesús Esteban-Parra
,
Jimy Dudhia
, and
Yolanda Castro-Díez

Abstract

This paper evaluates the Weather Research and Forecasting model (WRF) sensitivity to eight different combinations of cumulus, microphysics, and planetary boundary layer (PBL) parameterization schemes over a topographically complex region in southern Spain (Andalusia) for the period 1990–99. The WRF configuration consisted of a 10-km-resolution domain nested in a coarser domain driven by 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) data, with spectral nudging above the PBL employed over the latter domain. The model outputs have been compared at different time scales with an observational dataset that comprises 438 rain gauges and 152 temperature stations with records of both daily maximum and minimum temperatures. To reduce the “representation error,” the validation with observations has been performed using a multistep regionalization that distinguishes five precipitation regions and four temperature regions.

The analysis proves that both cumulus and PBL schemes have a crucial impact on the description of precipitation in Andalusia, whereas no noticeable differences between microphysics options were appreciated. Temperature is described similarly by all the configurations, except for the PBL choice affecting minimum values.

WRF provides a definite improvement over ERA-40 in the climate description in terms of frequency, spatial distribution, and intensity of extreme events. It also captures more accurately the annual cycle and reduces the biases and the RMSE for monthly precipitation, whereas only a minor enhancement of these features was obtained for monthly-mean maximum and minimum temperatures. The results indicate that WRF is able to correctly reproduce Andalusian climate and produces useful added-value information for climate studies.

Full access
Ethan D. Gutmann
,
Roy M. Rasmussen
,
Changhai Liu
,
Kyoko Ikeda
,
David J. Gochis
,
Martyn P. Clark
,
Jimy Dudhia
, and
Gregory Thompson

Abstract

Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50–200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the authors compare data typically used to derive spatial distributions of precipitation [Parameter-Elevation Regressions on Independent Slopes Model (PRISM)] to a high-resolution (2 km) weather model [Weather Research and Forecasting model (WRF)] under the current climate in the mountains of Colorado. It is shown that there are regions of significant difference in November–May precipitation totals (>300 mm) between the two, and possible causes for these differences are discussed. A simple statistical downscaling is then presented that is based on the 2-km WRF data applied to a series of regional climate models [North American Regional Climate Change Assessment Program (NARCCAP)], and the downscaled precipitation data are validated with observations at 65 snow telemetry (SNOTEL) sites throughout Colorado for the winter seasons from 1988 to 2000. The authors also compare statistically downscaled precipitation from a 36-km model under an imposed warming scenario with dynamically downscaled data from a 2-km model using the same forcing data. Although the statistical downscaling improved the domain-average precipitation relative to the original 36-km model, the changes in the spatial pattern of precipitation did not match the changes in the dynamically downscaled 2-km model. This study illustrates some of the uncertainties in applying statistical downscaling to future climate.

Full access
Roy Rasmussen
,
Kyoko Ikeda
,
Changhai Liu
,
David Gochis
,
Martyn Clark
,
Aiguo Dai
,
Ethan Gutmann
,
Jimy Dudhia
,
Fei Chen
,
Mike Barlage
,
David Yates
, and
Guo Zhang

Abstract

A high-resolution climate model (4-km horizontal grid spacing) is used to examine the following question: How will long-term changes in climate impact the partitioning of annual precipitation between evapotranspiration and runoff in the Colorado Headwaters?

This question is examined using a climate sensitivity approach in which eight years of current climate is compared to a future climate created by modifying the current climate signal with perturbation from the NCAR Community Climate System Model, version 3 (CCSM3), model forced by the A1B scenario for greenhouse gases out to 2050. The current climate period is shown to agree well with Snowpack Telemetry (SNOTEL) surface observations of precipitation (P) and snowpack, as well as streamflow and AmeriFlux evapotranspiration (ET) observations. The results show that the annual evaporative fraction (ET/P) for the Colorado Headwaters is 0.81 for the current climate and 0.83 for the future climate, indicating increasing aridity in the future despite a positive increase of precipitation. Runoff decreased by an average of 6%, reflecting the increased aridity.

Precipitation increased in the future winter by 12%, but decreased in the summer as a result of increased low-level inhibition to convection. The fraction of precipitation that fell as snow decreased from 0.83 in the current climate to 0.74 in the future. Future snowpack did not change significantly until January. From January to March the snowpack increased above ~3000 m MSL and decreased below that level. Snowpack decreased at all elevations in the future from April to July. The peak snowpack and runoff over the headwaters occurred 2–3 weeks earlier in the future simulation, in agreement with previous studies.

Full access
Edward G. Patton
,
Peter P. Sullivan
,
Branko Kosović
,
Jimy Dudhia
,
Larry Mahrt
,
Mark Žagar
, and
Tomislav Marić

Abstract

A combination of turbulence-resolving large-eddy simulations and observations are used to examine the influence of swell amplitude and swell propagation angle on surface drag. Based on the analysis a new surface roughness parameterization with nonequilibrium wave effects is proposed. The surface roughness accounts for swell amplitude and wavelength and its relative motion with respect to the mean wind direction. The proposed parameterization is tested in uncoupled three-dimensional Weather and Research Forecasting (WRF) simulations at grid sizes near 1 km where we explore potential implications of our modifications for two-way coupled atmosphere–wave models. Wind–wave misalignment likely explains the large scatter in observed nondimensional surface roughness under swell-dominated conditions. Andreas et al.’s relationship between friction velocity and the 10-m wind speed under predicts the increased drag produced by misaligned winds and waves. Incorporating wave-state (speed and direction) influences in parameterizations improves predictive skill. In a broad sense, these results suggest that one needs information on winds and wave state to upscale buoy measurements.

Open access
Song-You Hong
,
Kyo-Sun Sunny Lim
,
Ju-Hye Kim
,
Jeong-Ock Jade Lim
, and
Jimy Dudhia

Abstract

This study examines the relative importance of ice-phase microphysics and sedimentation velocity for hydrometeors in bulk microphysics schemes. The two bulk microphysics schemes having the same number of prognostic water substances, the Weather Research and Forecasting (WRF) Single-Moment 6-Class Microphysics Scheme (WSM6) and the Purdue–Lin scheme (PLIN), are evaluated for a 2D idealized storm case and for a 3D heavy rainfall event over Korea. The relative importance of microphysics and sedimentation velocity for ice particles is illuminated by the additional experiments that exchange the sedimentation velocity formula for graupel in the two schemes. In a 2D idealized storm simulation test bed, it is found that, relative to the PLIN scheme, the WSM6 scheme develops the storm late with weakened intensity because of a slower sedimentation velocity for graupel. Such a weakened intensity of precipitation also appears in a 3D model framework when the WSM6 scheme is used, in conjunction with the overall distribution of the precipitation band southward toward what was observed. The major reason is found to be the ice-phase microphysics of the WSM6 and related ice-cloud–radiation feedback, rather than the smaller terminal velocity for graupel in the WSM6 than in the PLIN scheme.

Full access
Sharanya J. Majumdar
,
Juanzhen Sun
,
Brian Golding
,
Paul Joe
,
Jimy Dudhia
,
Olivier Caumont
,
Krushna Chandra Gouda
,
Peter Steinle
,
Béatrice Vincendon
,
Jianjie Wang
, and
Nusrat Yussouf

Abstract

Improving the forecasting and communication of weather hazards such as urban floods and extreme winds has been recognized by the World Meteorological Organization (WMO) as a priority for international weather research. The WMO has established a 10-yr High-Impact Weather Project (HIWeather) to address global challenges and accelerate progress on scientific and social solutions. In this review, key challenges in hazard forecasting are first illustrated and summarized via four examples of high-impact weather events. Following this, a synthesis of the requirements, current status, and future research in observations, multiscale data assimilation, multiscale ensemble forecasting, and multiscale coupled hazard modeling is provided.

Full access
Pedro A. Jimenez
,
Joshua P. Hacker
,
Jimy Dudhia
,
Sue Ellen Haupt
,
Jose A. Ruiz-Arias
,
Chris A. Gueymard
,
Gregory Thompson
,
Trude Eidhammer
, and
Aijun Deng

Abstract

WRF-Solar is a specific configuration and augmentation of the Weather Research and Forecasting (WRF) Model designed for solar energy applications. Recent upgrades to the WRF Model contribute to making the model appropriate for solar power forecasting and comprise 1) developments to diagnose internally relevant atmospheric parameters required by the solar industry, 2) improved representation of aerosol–radiation feedback, 3) incorporation of cloud–aerosol interactions, and 4) improved cloud–radiation feedback. The WRF-Solar developments are presented together with a comprehensive characterization of the model performance for forecasting during clear skies. Performance is evaluated with numerical experiments using a range of different external and internal treatment of the atmospheric aerosols, including both a model-derived climatology of aerosol optical depth and temporally evolving aerosol optical properties from reanalysis products. The necessity of incorporating the influence of atmospheric aerosols to obtain accurate estimations of the surface shortwave irradiance components in clear-sky conditions is evident. Improvements of up to 58%, 76%, and 83% are found in global horizontal irradiance, direct normal irradiance, and diffuse irradiance, respectively, compared to a standard version of the WRF Model. Results demonstrate that the WRF-Solar model is an improved numerical tool for research and applications in support of solar energy.

Full access
Margaret A. LeMone
,
Fei Chen
,
Mukul Tewari
,
Jimy Dudhia
,
Bart Geerts
,
Qun Miao
,
Richard L. Coulter
, and
Robert L. Grossman

Abstract

Fair-weather data from the May–June 2002 International H2O Project (IHOP_2002) 46-km eastern flight track in southeast Kansas are compared to simulations using the advanced research version of the Weather Research and Forecasting model coupled to the Noah land surface model (LSM), to gain insight into how the surface influences convective boundary layer (CBL) fluxes and structure, and to evaluate the success of the modeling system in representing CBL structure and evolution. This offers a unique look at the capability of the model on scales the length of the flight track (46 km) and smaller under relatively uncomplicated meteorological conditions.

It is found that the modeled sensible heat flux H is significantly larger than observed, while the latent heat flux (LE) is much closer to observations. The slope of the best-fit line ΔLE/ΔH to a plot of LE as a function of H, an indicator of horizontal variation in available energy H + LE, for the data along the flight track, was shallower than observed. In a previous study of the IHOP_2002 western track, similar results were explained by too small a value of the parameter C in the Zilitinkevich equation used in the Noah LSM to compute the roughness length for heat and moisture flux from the roughness length for momentum, which is supplied in an input table; evidence is presented that this is true for the eastern track as well. The horizontal variability in modeled fluxes follows the soil moisture pattern rather than vegetation type, as is observed; because the input land use map does not capture the observed variation in vegetation. The observed westward rise in CBL depth is successfully modeled for 3 of the 4 days, but the actual depths are too high, largely because modeled H is too high. The model reproduces the timing of observed cumulus cloudiness for 3 of the 4 days.

Modeled clouds lead to departures from the typical clear-sky straight line relating surface H to LE for a given model time, making them easy to detect. With spatial filtering, a straight slope line can be recovered. Similarly, larger filter lengths are needed to produce a stable slope for observed fluxes when there are clouds than for clear skies.

Full access
Margaret A. LeMone
,
Fei Chen
,
Mukul Tewari
,
Jimy Dudhia
,
Bart Geerts
,
Qun Miao
,
Richard L. Coulter
, and
Robert L. Grossman

Abstract

Fair-weather data along the May–June 2002 International H2O Project (IHOP_2002) eastern track and the nearby Argonne Boundary Layer Experiments (ABLE) facility in southeast Kansas are compared to numerical simulations to gain insight into how the surface influences convective boundary layer (CBL) structure, and to evaluate the success of the modeling system in replicating the observed behavior. Simulations are conducted for 4 days, using the Advanced Research version of the Weather Research and Forecasting (WRF) model coupled to the Noah land surface model (LSM), initialized using the High-Resolution Land Data Assimilation System (HRLDAS). Because the observations focus on phenomena less than 60 km in scale, the model is run with 1-km grid spacing, offering a critical look at high-resolution model behavior in an environment uncomplicated by precipitation.

The model replicates the type of CBL structure on scales from a few kilometers to ∼100 km, but some features at the kilometer scales depend on the grid spacing. Mesoscale (tens of kilometers) circulations were clearly evident on 2 of the 4 days (30 May and 20 June), clearly not evident on 1 day (22 June), with the situation for the fourth day (17 June) ambiguous. Both observed and modeled surface-heterogeneity-generated mesoscale circulations are evident for 30 May. On the other hand, 20 June satellite images show north-northwest–south-southeast cloud streets (rolls) modulated longitudinally, presumably by tropospheric gravity waves oriented normal to the roll axis, creating northeast–southwest ridges and valleys spaced 50–100 km apart. Modeled cloud streets showed similar longitudinal modulation, with the associated two-dimensional structure having maximum amplitude above the CBL and no relationship to the CBL temperature distribution; although there were patches of mesoscale vertical velocity correlated with CBL temperature. On 22 June, convective rolls were the dominant structure in both model and observations.

For the 3 days for which satellite images show cloud streets, WRF produces rolls with the right orientation and wavelength, which grows with CBL depth. Modeled roll structures appeared for the range of CBL depth to Obukhov length ratios (−zi /L) associated with rolls. However, sensitivity tests show that the roll wavelength is also related to the grid spacing, and the modeled convection becomes more cellular with smaller grid spacing.

Full access
Anna C. Fitch
,
Joseph B. Olson
,
Julie K. Lundquist
,
Jimy Dudhia
,
Alok K. Gupta
,
John Michalakes
,
Idar Barstad
, and
Cristina L. Archer
Full access