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Barry H. Lynn
,
Alexander P. Khain
,
Jimy Dudhia
,
Daniel Rosenfeld
,
Andrei Pokrovsky
, and
Axel Seifert

Abstract

Considerable research investments have been made to improve the accuracy of forecasting precipitation systems in cloud-resolving, mesoscale atmospheric models. Yet, despite a significant improvement in model grid resolution and a decrease in initial condition uncertainty, the accurate prediction of precipitation amount and distribution still remains a difficult problem. Now, the development of a fast version of spectral (bin) microphysics (SBM Fast) offers significant potential for improving the description of precipitation-forming processes in mesoscale atmospheric models.

The SBM Fast is based on solving a system of equations for size distribution functions for water drops and three types of ice crystals (plates, columns, and dendrites), as well as snowflakes, graupel, and hail/frozen drops. Ice processes are represented by three size distributions, instead of six in the original SBM code. The SBM uses first principles to simulate microphysical processes such as diffusional growth and collision. A budget for aerosols is used to obtain the spectrum of condensation nuclei, which is used to obtain the initial drop spectrum. Hence, SBM allows one to take into account aerosol effects on precipitation, and corresponding cloud effects on the atmospheric aerosol concentration and distribution. SBM Fast has been coupled with the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5), which allows SBM Fast to simulate microphysics within a realistic, time-varying mesoscale environment.

This paper describes the first three-dimensional SBM mesoscale model and presents results using 1-km resolution to simulate initial development of a cloud system over Florida on 27 July 1991. The focus is on initial cloud development along the west coast, just prior to sea-breeze formation. The results indicate that the aerosol concentration had a very important impact on cloud dynamics, microphysics, and rainfall.

Vertical cross sections of clouds obtained using SBM Fast are compared to those from a version of the “Reisner2” bulk-parameterization scheme that uses the Kessler autoconversion formula. The results show that this version of “Reisner2” produced vertically upright clouds that progressed very quickly from initial cloud formation to raindrop formation. In contrast, clouds obtained using SBM were relatively long lasting with greater production of stratiform clouds.

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Barry H. Lynn
,
Alexander P. Khain
,
Jimy Dudhia
,
Daniel Rosenfeld
,
Andrei Pokrovsky
, and
Axel Seifert

Abstract

Spectral (bin) microphysics (SBM) has been implemented into the three-dimensional fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5). The new model was used to simulate a squall line that developed over Florida on 27 July 1991. It is shown that SBM reproduces precipitation rate, rain amounts, and location, radar reflectivity, and cloud structure much better than bulk parameterizations currently implemented in MM5.

Sensitivity tests show the importance of (i) raindrop breakup, (ii) in-cloud turbulence, (iii) different aerosol concentrations, and (iv) inclusion of scavenging of aerosols. Breakup decreases average and maximum rainfall. In-cloud turbulence enhances particle drop collision rates and increases rain rates. A “continental” aerosol concentration produces a much larger maximum rainfall rate versus that obtained with “maritime” aerosol concentration. At the same time accumulated rain is larger with maritime aerosol concentration. The scavenging of aerosols by nucleating water droplets strongly affected the concentration of aerosols in the atmosphere.

The spectral (bin) microphysics mesoscale model can potentially be used for studies of specific phenomena such as severe storms, winter storms, tropical cyclones, etc. The more realistic reproduction of cloud structure than that obtained with bulk parameterization implies that the model will be more useful for remote sensing applications and in the development of advanced rain retrieval algorithms. The model can also simulate the effect of cloud seeding on rain production.

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Jared A. Lee
,
Pedro A. Jiménez
,
Jimy Dudhia
, and
Yves-Marie Saint-Drenan

Abstract

Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson–Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.

Significance Statement

Particles in the atmosphere called aerosols, which can include dust, smoke, sea salt, sulfates, black carbon, and organic carbon, absorb and scatter incoming sunlight. Improving the representation of aerosols in numerical weather prediction models reduces forecast errors in solar irradiance at ground level, particularly direct normal irradiance, during cloud-free conditions. This in turn should result in improved accuracy of solar power forecasts, especially for concentrated solar power (CSP) plants. CSP plants tend to be built in more arid, less cloudy regions that are also prone to dust loading, so accurate aerosol forecasts are particularly relevant. Comparing four representations of aerosols in the WRF-Solar model over eight months of forecasts across the United States reveals substantial differences in clear-sky irradiance forecast skill.

Free access
Pedro A. Jiménez
,
Jaemo Yang
,
Ju-Hye Kim
,
Manajit Sengupta
, and
Jimy Dudhia

Abstract

WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol–cloud–radiation processes. This flexibility can be argued to make it more difficult to improve the model’s performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing the WRF-Solar performance.

Full access
Andrea Zonato
,
Alberto Martilli
,
Pedro A. Jimenez
,
Jimy Dudhia
,
Dino Zardi
, and
Lorenzo Giovannini

Abstract

A new one-dimensional 1.5-order planetary boundary layer (PBL) scheme, based on the K–ε turbulence closure applied to the Reynolds-averaged Navier–Stokes (RANS) equations, is developed and implemented within the Weather Research and Forecasting (WRF) Model. The new scheme includes an analytic solution of the coupled equations for turbulent kinetic energy and dissipation rate. Different versions of the PBL scheme are proposed, with increasing levels of complexity, including a model for the calculation of the Prandtl number, a correction to the dissipation rate equation, and a prognostic equation for the temperature variance. Five different idealized cases are tested: four of them explore convective conditions, and they differ in initial thermal stratification and terrain complexity, while one simulates the very stable boundary layer case known as GABLS. For each case study, an ensemble of different large-eddy simulations (LES) is taken as reference for the comparison with the novel PBL schemes and other state-of-the-art 1- and 1.5-order turbulence closures. Results show that the new PBL K–ε scheme brings improvements in all the cases tested in this study. Specifically, the more significant are obtained with the turbulence closure including a prognostic equation for the temperature variance. Moreover, the largest benefits are obtained for the idealized cases simulating a typical thermal circulation within a two-dimensional valley. This suggests that the use of prognostic equations for dissipation rate and temperature variance, which take into account their transport and history, is particularly important with the increasing complexity of PBL dynamics.

Open access
Xabier Pedruzo-Bagazgoitia
,
Pedro A. Jiménez
,
Jimy Dudhia
, and
Jordi Vilà-Guerau de Arellano

Abstract

This study presents a systematic analysis of convective parameterizations performance with interactive radiation, microphysics, and surface on an idealized day with shallow convection. To this end, we analyze a suite of mesoscale numerical experiments (i.e., with parameterized turbulence). In the first set, two different convection schemes represent shallow convection at a 9-km resolution. These experiments are then compared with model results omitting convective parameterizations at 9- and 3-km horizontal resolution (gray zone). Relevant in our approach is to compare the results against two simulations by different large-eddy simulation (LES) models. Results show that the mesoscale experiments, including the 3-km resolution, are unable to adequately represent the timing, intensity, height, and extension of the shallow cumulus field. The main differences with LES experiments are the following: a too late onset, too high cloud base, and a too early transport of moisture too high, overestimating the second cloud layer. Related to this, both convective parameterizations produce warm and dry biases of up to 2 K and 2 g kg−1, respectively, in the cloud layer. This misrepresentation of the cloud dynamics leads to overestimated shortwave radiation variability, both spacewise and timewise. Domain-averaged shortwave radiation at the surface, however, compares satisfactorily with LES. The shortwave direct and diffuse partition is misrepresented by the convective parameterizations with an underestimation (overestimation) of diffuse (direct) radiation both locally and, by a relative 40% (10%), of the domain average.

Open access
Qingnong Xiao
,
Ying-Hwa Kuo
,
Zaizhong Ma
,
Wei Huang
,
Xiang-Yu Huang
,
Xiaoyan Zhang
,
Dale M. Barker
,
John Michalakes
, and
Jimy Dudhia

Abstract

The tangent linear and adjoint of an adiabatic version of the Weather Research and Forecasting (WRF) Model with its Advanced Research WRF (ARW) dynamic core have been developed. The source-to-source automatic differentiation tool [i.e., the Transformation of Algorithm (TAF) in FORTRAN] was used in the development. Tangent linear and adjoint checks of the developed adiabatic WRF adjoint modeling system (WAMS) were conducted, and all necessary correctness verification procedures were passed. As the first application, the adiabatic WAMS was used to study the adjoint sensitivity of a severe windstorm in Antarctica. Linearity tests indicated that an adjoint-based sensitivity study with the Antarctic Mesoscale Prediction System (AMPS) 90-km domain configuration for the windstorm is valid up to 24 h. The adjoint-based sensitivity calculation with adiabatic WAMS identified sensitive regions for the improvement of the 24-h forecast of the windstorm. It is indicated that the windstorm forecast largely relies on the model initial conditions in the area from the south part of the Trans-Antarctic Mountains to West Antarctica and between the Ross Ice Shelf and the South Pole. Based on the sensitivity analysis, the southerly or southeasterly wind at lower levels in the sensitivity region should be larger, the cyclone should be stronger, and the atmospheric stratification should be more stable over the north slope of the Trans-Antarctic Mountain to the Ross Ice Shelf, than the AMPS analyses. By constructing pseudo-observations in the sensitivity region using the gradient information of forecast windstorm intensity around McMurdo, the model initial conditions are revised with the WRF three-dimensional variational data assimilation, which leads to significant improvement in the prediction of the windstorm. An adjoint sensitivity study is an efficient way to identify sensitivity regions in order to collect more observations in the region for better forecasts in a specific aspect of interest.

Full access
Pedro A. Jiménez
,
J. Fidel González-Rouco
,
Elena García-Bustamante
,
Jorge Navarro
,
Juan P. Montávez
,
Jordi Vilà-Guerau de Arellano
,
Jimy Dudhia
, and
Antonio Muñoz-Roldan

Abstract

This study analyzes the daily-mean surface wind variability over an area characterized by complex topography through comparing observations and a 2-km-spatial-resolution simulation performed with the Weather Research and Forecasting (WRF) model for the period 1992–2005. The evaluation focuses on the performance of the simulation to reproduce the wind variability within subregions identified from observations over the 1999–2002 period in a previous study. By comparing with wind observations, the model results show the ability of the WRF dynamical downscaling over a region of complex terrain. The higher spatiotemporal resolution of the WRF simulation is used to evaluate the extent to which the length of the observational period and the limited spatial coverage of observations condition one’s understanding of the wind variability over the area. The subregions identified with the simulation during the 1992–2005 period are similar to those identified with observations (1999–2002). In addition, the reduced number of stations reasonably represents the spatial wind variability over the area. However, the analysis of the full spatial dimension simulated by the model suggests that observational coverage could be improved in some subregions. The approach adopted here can have a direct application to the design of observational networks.

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Greg M. McFarquhar
,
Henian Zhang
,
Gerald Heymsfield
,
Jeffrey B. Halverson
,
Robbie Hood
,
Jimy Dudhia
, and
Frank Marks Jr.

Abstract

Fine-resolution simulations of Hurricane Erin are conducted using the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to investigate roles of thermodynamic, boundary layer, and microphysical processes on Erin’s structure and evolution. Choice of boundary layer scheme has the biggest impact on simulations, with the minimum surface pressure (P min) averaged over the last 18 h (when Erin is relatively mature) varying by over 20 hPa. Over the same period, coefficients used to describe graupel fall speeds (Vg ) affect P min by up to 7 hPa, almost equivalent to the maximum 9-hPa difference between microphysical parameterization schemes; faster Vg and schemes with more hydrometeor categories generally give lower P min. Compared to radar reflectivity factor (Z) observed by the NOAA P-3 lower fuselage radar and the NASA ER-2 Doppler radar (EDOP) in Erin, all simulations overpredict the normalized frequency of occurrence of Z larger than 40 dBZ and underpredict that between 20 and 40 dBZ near the surface; simulations overpredict Z larger than 25 to 30 dBZ and underpredict that between 15 and 25 or 30 dBZ near the melting layer, the upper limit depending on altitude. Brightness temperatures (Tb ) computed from modeled fields at 37.1- and 85.5-GHz channels that respond to scattering by graupel-size ice show enhanced scattering, mainly due to graupel, compared to observations. Simulated graupel mixing ratios are about 10 times larger than values observed in other hurricanes. For the control run at 6.5 km averaged over the last 18 simulated hours, Doppler velocities computed from modeled fields (V dop) greater than 5 m s−1 make up 12% of Erin’s simulated area for the base simulation but less than 2% of the observed area. In the eyewall, 5% of model updrafts above 9 km are stronger than 10 m s−1, whereas statistics from other hurricanes show that 5% of updrafts are stronger than only 5 m s−1. Variations in distributions of Z, vertical motion, and graupel mixing ratios between schemes are not sufficient to explain systematic offsets between observations and models. A new iterative condensation scheme, used with the Reisner mixed-phase microphysics scheme, limits unphysical increases of equivalent potential temperature associated with many condensation schemes and reduces the frequency of Z larger than 50 dBZ, but has minimal effect on Z below 50 dBZ, which represent 95% of the modeled hurricane rain area. However, the new scheme changes the Erin simulations in that 95% of the updrafts are weaker than 5 m s−1 and P min is up to 12 hPa higher over the last 18 simulated hours.

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Pedro A. Jiménez
,
Jordi Vilà-Guerau de Arellano
,
J. Fidel González-Rouco
,
Jorge Navarro
,
Juan P. Montávez
,
Elena García-Bustamante
, and
Jimy Dudhia

Abstract

Variations in the diurnal wind pattern associated with heat waves and drought conditions are investigated climatologically at a regional level (northeast of the Iberian Peninsula). The study, based on high-density observational evidence and fine spatial-scale mesoscale modeling for the 1992–2004 period, shows that wind speed can decrease up to 22% under situations characterized by extremely high temperatures and severe drought, such as the European summer of 2003. By examining the role of the different atmospheric scales of motion that determine the wind diurnal variability, it is found that the 2003 synoptic conditions are the main driver for changes in the wind speed field. In turn, these changes are modulated by mesoscale circulations influenced by the soil moisture availability. The results have implications for broad regional modeling studies of current climate and climate change simulations in as much as the study demonstrates that a correct representation of local soil moisture conditions impacts atmospheric circulation and therefore the regional climate state.

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