scholarly journals A Methodology for Verifying Cloud Forecasts with VIIRS Imagery and Derived Cloud Products—A WRF Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 521 ◽  
Author(s):  
Keith D. Hutchison ◽  
Barbara D. Iisager ◽  
Sudhakar Dipu ◽  
Xiaoyan Jiang ◽  
Johannes Quaas ◽  
...  
Keyword(s):  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Region Of Interest ◽  
Weather Research And Forecasting ◽  
The North ◽  
Infrared Imager ◽  
Internal Procedures ◽  
Initial Results

A methodology is presented to evaluate the accuracy of cloud cover fraction (CCf) forecasts generated by numerical weather prediction (NWP) and climate models. It is demonstrated with a case study consisting of simulations from the Weather Research and Forecasting (WRF) model. In this study, since the WRF CCf forecasts were initialized with reanalysis fields from the North American Mesoscale (NAM) Forecast System, the characteristics of the NAM CCf products were also evaluated. The procedures relied extensively upon manually-generated, binary cloud masks created from VIIRS (Visible Infrared Imager Radiometry Suite) imagery, which were subsequently converted into CCf truth at the resolution of the NAM and WRF gridded data. The initial results from the case study revealed biases toward under-clouding in the NAM CCf analyses and biases toward over-clouding in the WRF CCf products. These biases were evident in images created from the gridded NWP products when compared to VIIRS imagery and CCf truth data. Thus, additional simulations were completed to help assess the internal procedures used in the WRF model to translate moisture forecast fields into layered CCf products. Two additional sets of WRF CCf 24 h forecasts were generated for the region of interest using WRF restart files. One restart file was updated with CCf truth data and another was not changed. Over-clouded areas in the updated WRF restart file that were reduced with an update of the CCf truth data became over-clouded again in the WRF 24 h forecast, and were nearly identical to those from the unchanged restart file. It was concluded that the conversion of WRF forecast fields into layers of CCf products deserves closer examination in a future study.

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

WRF model moisture adjustment method; a case study with winter-time cloudy biases in Xinjiang, China.

2021 ◽  
Author(s):  
Guiting Song ◽  
Robert Huva ◽  
Yu Xing ◽  
Xiaohui Zhong
Keyword(s):  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Adjustment Method ◽  
Surface Irradiance ◽  
Convective Processes ◽  
Systematic Biases ◽  
Nwp Model ◽  
Winter Time

AbstractFor most locations on Earth the ability of a Numerical Weather Prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometres NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or over-prediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting model (WRF) for a location in Xinjiang, China. After extensive optimisation of the configuration of the WRF model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a Root Mean Square Error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

On the effective resolution of WRF simulations at microscale grid resolution.

2021 ◽  
Author(s):  
Pedro Bolgiani ◽  
Javier Díaz-Fernández ◽  
Lara Quitián-Hernández ◽  
Mariano Sastre ◽  
Daniel Santos-Muñoz ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Research Community ◽  
Weather Research And Forecasting ◽  
Large Uncertainty ◽  
Partial Explanation ◽  
Synoptic Conditions ◽  
Large Effort ◽  
Numerical Weather Prediction Models

<p>As the computational capacity has been largely improved in the last decades, the grid configuration of numerical weather prediction models has stepped into microscale resolutions. Even if mesoscale models are not originally designed to reproduce fine scale phenomena, a large effort is being made by the research community to improve and adapt these systems. However, reasonable doubts exist regarding the ability of the models to forecast this type of events, due to the unfit parametrizations and the appearance of instabilities and lack of sensitivity in the variables. Here, the Weather Research and Forecasting (WRF) model effective resolution is evaluated for several situations and grid resolutions. This is achieved by assessing the curve of dissipation for the wind kinetic energy. Results show that the simulated energy spectrum responds to different synoptic conditions. Nevertheless, when the model is forced into microscale grid resolutions the dissipation curves present an unrealistic atmospheric energy. This may be a partial explanation to the aforementioned issues and imposes a large uncertainty to forecasting at these resolutions.</p>

scholarly journals Mesoscale Simulations of Australian Direct Normal Irradiance, Featuring an Extreme Dust Event

2018 ◽  
Vol 57 (3) ◽  
pp. 493-515 ◽  
Author(s):  
S. K. Mukkavilli ◽  
A. A. Prasad ◽  
R. A. Taylor ◽  
A. Troccoli ◽  
M. J. Kay
Keyword(s):  
Radiative Transfer ◽  
Climate Models ◽  
Global Climate ◽  
Weather Prediction ◽  
Wrf Model ◽  
Global Climate Models ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Dust Event ◽  
Direct Normal Irradiance

AbstractDirect normal irradiance (DNI) is the main input for concentrating solar power (CSP) technologies—an important component in future energy scenarios. DNI forecast accuracy is sensitive to radiative transfer schemes (RTSs) and microphysics in numerical weather prediction (NWP) models. Additionally, NWP models have large regional aerosol uncertainties. Dust aerosols can significantly attenuate DNI in extreme cases, with marked consequences for applications such as CSP. To date, studies have not compared the skill of different physical parameterization schemes for predicting hourly DNI under varying aerosol conditions over Australia. The authors address this gap by aiming to provide the first Weather and Forecasting (WRF) Model DNI benchmarks for Australia as baselines for assessing future aerosol-assimilated models. Annual and day-ahead simulations against ground measurements at selected sites focusing on an extreme dust event are run. Model biases are assessed for five shortwave RTSs at 30- and 10-km grid resolutions, along with the Thompson aerosol-aware scheme in three different microphysics configurations: no aerosols, fixed optical properties, and monthly climatologies. From the annual simulation, the best schemes were the Rapid Radiative Transfer Model for global climate models (RRTMG), followed by the new Goddard and Dudhia schemes, despite the relative simplicity of the latter. These top three RTSs all had 1.4–70.8 W m−2 lower mean absolute error than persistence. RRTMG with monthly aerosol climatologies was the best combination. The extreme dust event had large DNI mean bias overpredictions (up to 4.6 times), compared to background aerosol results. Dust storm–aware DNI forecasts could benefit from RRTMG with high-resolution aerosol inputs.

scholarly journals Evaluation of PBL Parameterizations in WRF at Subkilometer Grid Spacings: Turbulence Statistics in the Dry Convective Boundary Layer

2016 ◽  
Vol 144 (3) ◽  
pp. 1161-1177 ◽  
Author(s):  
Hyeyum Hailey Shin ◽  
Jimy Dudhia
Keyword(s):  
Boundary Layer ◽  
Mass Flux ◽  
Weather Prediction ◽  
Wrf Model ◽  
Eddy Diffusivity ◽  
Weather Research And Forecasting ◽  
Turbulence Statistics ◽  
Convective Boundary ◽  
Large Eddies ◽  
Horizontal Grid

Abstract Planetary boundary layer (PBL) parameterizations in mesoscale models have been developed for horizontal resolutions that cannot resolve any turbulence in the PBL, and evaluation of these parameterizations has been focused on profiles of mean and parameterized flux. Meanwhile, the recent increase in computing power has been allowing numerical weather prediction (NWP) at horizontal grid spacings finer than 1 km, at which kilometer-scale large eddies in the convective PBL are partly resolvable. This study evaluates the performance of convective PBL parameterizations in the Weather Research and Forecasting (WRF) Model at subkilometer grid spacings. The evaluation focuses on resolved turbulence statistics, considering expectations for improvement in the resolved fields by using the fine meshes. The parameterizations include four nonlocal schemes—Yonsei University (YSU), asymmetric convective model 2 (ACM2), eddy diffusivity mass flux (EDMF), and total energy mass flux (TEMF)—and one local scheme, the Mellor–Yamada–Nakanishi–Niino (MYNN) level-2.5 model. Key findings are as follows: 1) None of the PBL schemes is scale-aware. Instead, each has its own best performing resolution in parameterizing subgrid-scale (SGS) vertical transport and resolving eddies, and the resolution appears to be different between heat and momentum. 2) All the selected schemes reproduce total vertical heat transport well, as resolved transport compensates differences of the parameterized SGS transport from the reference SGS transport. This interaction between the resolved and SGS parts is not found in momentum. 3) Those schemes that more accurately reproduce one feature (e.g., thermodynamic transport, momentum transport, energy spectrum, or probability density function of resolved vertical velocity) do not necessarily perform well for other aspects.

scholarly journals Potential of a geostationary geoCARB mission to estimate surface emissions of CO<sub>2</sub>, CH<sub>4</sub> and CO in a polluted urban environment: case study Shanghai

2016 ◽  
Vol 9 (9) ◽  
pp. 4633-4654 ◽  
Author(s):  
Denis M. O'Brien ◽  
Igor N. Polonsky ◽  
Steven R. Utembe ◽  
Peter J. Rayner
Keyword(s):  
Urban Environment ◽  
Mass Density ◽  
Wrf Model ◽  
Trace Gas ◽  
Weather Research And Forecasting ◽  
Retrieval Algorithm ◽  
Remotely Sensed Data ◽  
Ice Clouds ◽  
Instrument Noise

Abstract. This paper describes a numerical experiment to test the ability of the proposed geoCARB satellite to estimate emissions of trace gases (CO2, CH4 and CO) in the polluted urban environment of Shanghai. The meteorology over Shanghai is simulated with the Weather Research and Forecasting (WRF) model for a 9-day period in August 2010. The meteorology includes water and ice clouds. The chemistry version of WRF (WRF-Chem V3.6.1) is used to predict the chemical composition, mass density and number density of aerosol species. Spectra in the bands measured by geoCARB are calculated, including the effects of polarisation and multiple scattering of radiation by clouds, aerosols and molecules. Instrument noise is added, and column-averaged trace-gas mole fractions are estimated from the noisy spectra using an algorithm based on that for the Greenhouse Gases Observing Satellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) but adapted to geoCARB. As expected, the high aerosol loadings are challenging. However, when the retrieval algorithm is provided with regionally adjusted aerosol optical properties, as might be determined from observations of dark targets within the field of regard, the accuracies of retrieved concentrations are comparable to those reported earlier for geoCARB. Statistics of the errors in the retrieved column-averaged concentrations are used to predict the reduction in uncertainty of surface emissions possible with remotely sensed data.

scholarly journals Using the Weather Research and Forecasting (WRF) Model for Precipitation Forecasting in an Andean Region with Complex Topography

Atmosphere ◽  
2018 ◽  
Vol 9 (8) ◽  
pp. 304 ◽  
Author(s):  
Gonzalo Yáñez-Morroni ◽  
Jorge Gironás ◽  
Marta Caneo ◽  
Rodrigo Delgado ◽  
René Garreaud
Keyword(s):  
Land Surface ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Lead Times ◽  
Complex Topography ◽  
Front Range ◽  
Meteorological Forcing ◽  
Rainfall Events ◽  
Weather Research

The Weather Research and Forecasting (WRF) model has been successfully used in weather prediction, but its ability to simulate precipitation over areas with complex topography is not optimal. Consequently, WRF has problems forecasting rainfall events over Chilean mountainous terrain and foothills, where some of the main cities are located, and where intense rainfall occurs due to cutoff lows. This work analyzes an ensemble of microphysics schemes to enhance initial forecasts made by the Chilean Weather Agency in the front range of Santiago. We first tested different vertical levels resolution, land use and land surface models, as well as meteorological forcing (GFS/FNL). The final ensemble configuration considered three microphysics schemes and lead times over three rainfall events between 2015 and 2017. Cutoff low complex meteorological characteristics impede the temporal simulation of rainfall properties. With three days of lead time, WRF properly forecasts the rainiest N-hours and temperatures during the event, although more accuracy is obtained when the rainfall is caused by a meteorological frontal system. Finally, the WSM6 microphysics option had the best performance, although further analysis using other storms and locations in the area are needed to strengthen this result.

Sensitivity of 24-h Forecast Dryline Position and Structure to Boundary Layer Parameterizations in Convection-Allowing WRF Model Simulations

2015 ◽  
Vol 30 (3) ◽  
pp. 613-638 ◽  
Author(s):  
Adam J. Clark ◽  
Michael C. Coniglio ◽  
Brice E. Coffer ◽  
Greg Thompson ◽  
Ming Xue ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Initial Conditions ◽  
Layer Structure ◽  
Vertical Mixing ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
The North ◽  
Boundary Layer Processes ◽  
Moisture Profiles ◽  
Dry Bias

Abstract Recent NOAA Hazardous Weather Testbed Spring Forecasting Experiments have emphasized the sensitivity of forecast sensible weather fields to how boundary layer processes are represented in the Weather Research and Forecasting (WRF) Model. Thus, since 2010, the Center for Analysis and Prediction of Storms has configured at least three members of their WRF-based Storm-Scale Ensemble Forecast (SSEF) system specifically for examination of sensitivities to parameterizations of turbulent mixing, including the Mellor–Yamada–Janjić (MYJ); quasi-normal scale elimination (QNSE); Asymmetrical Convective Model, version 2 (ACM2); Yonsei University (YSU); and Mellor–Yamada–Nakanishi–Niino (MYNN) schemes (hereafter PBL members). In postexperiment analyses, significant differences in forecast boundary layer structure and evolution have been observed, and for preconvective environments MYNN was found to have a superior depiction of temperature and moisture profiles. This study evaluates the 24-h forecast dryline positions in the SSEF system PBL members during the period April–June 2010–12 and documents sensitivities of the vertical distribution of thermodynamic and kinematic variables in near-dryline environments. Main results include the following. Despite having superior temperature and moisture profiles, as indicated by a previous study, MYNN was one of the worst-performing PBL members, exhibiting large eastward errors in forecast dryline position. During April–June 2010–11, a dry bias in the North American Mesoscale Forecast System (NAM) initial conditions largely contributed to eastward dryline errors in all PBL members. An upgrade to the NAM and assimilation system in October 2011 apparently fixed the dry bias, reducing eastward errors. Large sensitivities of CAPE and low-level shear to the PBL schemes were found, which were largest between 1.0° and 3.0° to the east of drylines. Finally, modifications to YSU to decrease vertical mixing and mitigate its warm and dry bias greatly reduced eastward dryline errors.

Secondary Ice Production in Antarctic Clouds: a process neglected in large-scale models

2020 ◽  
Author(s):  
Georgia Sotiropoulou ◽  
Etienne Vignon ◽  
Gillian Young ◽  
Thomas Lachlan-Cope ◽  
Alexis Berne ◽  
...  
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weddell Sea ◽  
Microphysics Scheme ◽  
Ice Multiplication ◽  
Ice Production ◽  
Low Sensitivity ◽  
The Impact

&lt;p&gt;In-situ measurements of Antarctic clouds frequently show that ice crystal number concentrations &amp;#160;are much higher than the available ice-nucleating particles, suggesting that Secondary Ice Production (SIP) may be active. Here we investigate the impact of two SIP mechanisms, Hallett-Mossop (H-M)and collisional break-up (BR), on a case from the Microphysics of Antarctic Clouds (MAC) campaign in Weddell Sea using the Weather and Research Forecasting (WRF) model. H-M is already included in the default version of the Morrison microphysics scheme in WRF; for BR we implement different parameterizations and compare their performance. H-M alone is not effective enough to reproduce the observed concentrations. In contrast, BR can result in realistic ice multiplication, independently of whether H-M is active or not. In particular, the Phillips parameterization results in very good agreement with observations, but its performance depends on the prescribed rimed fraction of the colliding ice particles. Finally, our results show low sensitivity to primary ice nucleation, as long as there are enough primary ice crystals to initiate ice-ice collisions. Our findings suggest that BR is a potentially important SIP mechanism in the pristine Antarctic atmosphere that is currently not represented in weather-prediction and climate models.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document