Evaluation and Usefulness of Lightning Forecasts Made with Lightning Parameterization Schemes Coupled with the WRF Model

ABSTRACTIn this paper, we have proposed a method of sea fog prediction using model sea surface wind (SSW) data and hourly optical satellite data with 500 m spatial resolution. Three sea fog cases in the Yellow Sea were selected from Geostationary Ocean Color Imager (GOCI) images, and their shifts were determined from the fog cluster centroid displacements at successive hours. The 4 km resolution Weather Research and Forecasting (WRF) model SSW were used to predict sea fog shifts. The sea fog prediction results were verified using validation indices like probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The predictions started with high initial skill scores (POD = 0.76, FAR = 0.18, and CSI = 0.66), and showed values 0.59, 0.30, and 0.46 for POD, FAR, and CSI, respectively for predictions after two hours. After three hours their values still remained close to the medians though decreased, but afterwards decreased considerably for POD and CSI. Thus the method is found suitable for short-term prediction of sea fog. Additionally, trajectory sensitivities were compared between 20 and 4 km resolution WRF which, in general, resulted in less errors for 4 km WRF model SSW.

Predicting lightning activity in Greece with the Weather Research and Forecasting (WRF) model

Abstract. In the present study, we have used the Weather Research and Forecasting (WRF) model to simulate the features associated with a severe thunderstorm observed over Gadanki (13.5° N, 79.2° E), over southeast India, on 21 May 2008 and examined its sensitivity to four different microphysical (MP) schemes (Thompson, Lin, WSM6 and Morrison). We have used the WRF model with three nested domains with the innermost domain of 2 km grid spacing with explicit convection. The model was integrated for 36 h with the GFS initial conditions of 00:00 UTC, 21 May 2008. For validating simulated features of the thunderstorm, we have considered the vertical wind measurements made by the Indian MST radar installed at Gadanki, reflectivity profiles by the Doppler Weather Radar at Chennai, and automatic weather station data at Gadanki. There are major differences in the simulations of the thunderstorm among the MP schemes, in spite of using the same initial and boundary conditions and model configuration. First of all, all the four schemes simulated severe convection over Gadanki almost an hour before the observed storm. The DWR data suggested passage of two convective cores over Gadanki on 21 May, which was simulated by the model in all the four MP schemes. Comparatively, the Thompson scheme simulated the observed features of the updraft/downdraft cores reasonably well. However, all the four schemes underestimated strength and vertical extend of the updraft cores. The MP schemes also showed problems in simulating the downdrafts associated with the storm. While the Thompson scheme simulated surface rainfall distribution closer to observations, the other three schemes overestimated observed rainfall. However, all the four MP schemes simulated the surface wind variations associated with the thunderstorm reasonably well. The model simulated reflectivity profiles were consistent with the observed reflectivity profile, showing two convective cores. These features are consistent with the simulated condensate profiles, which peaked around 5–6 km. As the results are dependent on initial conditions, in simulations with different initial conditions, different schemes may become closer to observations. The present study suggests not only large sensitivity but also variability of the microphysical schemes in the simulations of the thunderstorm. The study also emphasizes the need for a comprehensive observational campaign using multi-observational platforms to improve the parameterization of the cloud microphysics and land surface processes over the Indian region.

The low-echo centroid (LEC) storm, characterized by the dominance of warm-rain processes and high precipitation efficiency (Vitale & Ryan, 2013), is generally associated with high-intensity rainfall events in tropical and subtropical regions (Hamada et al. 2015). While many double-moment microphysical parameterization schemes are primarily designed for deep convection or cold-rain processes, research on their performance in simulating LEC warm-cloud precipitation systems is limited. In this study, we investigate an extreme rainfall case that occurred on 20 July 2016 in northern China, resulting in over 600 mm of maximum 24-hour accumulated rainfall. According to radar observations, the case is characterized by LEC structure. Using the Weather Research and Forecasting (WRF) model with 4 km grid spacing, we simulate this event employing the Morrison, Thompson, and Milbrandt-Yau double-moment microphysics schemes. Evaluation based on simulated infrared brightness temperature (BT) using a radiative transfer model and simulated reflectivity reveals that while different microphysics schemes generally predict rainfall amount, location, and propagation accurately, they fail to replicate the three-dimensional cloud structures. The simulated convective cores (>35dBZ) are higher than -10 °C, indicating active cold-cloud processes, while the observed LEC suggests the dominance of warm-cloud processes. The model produces an excessive number of upper-level clouds and overshooting clouds, also overpredict the cloud-top height. Sensitivity experiments show that simulated brightness temperatures are primarily influenced by the concentration of cloud ice particles. Morrison and Milbrandt-Yau microphysics schemes produces an overabundance of cloud ice particles in the upper layer, leading to the overproduction of uppe-level cloud and incorrect representation of cloud top height. Warm-rain processes are not fully developed, and the cold-rain processes are not effectively restrained, resulting in unrealistic cloud structure. By adjusting microphysical processes in the schemes, such as increasing cloud water number concentration, the simulated convective cores align more closely with the observed ones. In summary, while the current microphysics schemes effectively simulate rainfall intensity and propagation, there is a clear need for improvement in simulating the cloud particle distribution and vertical structure of LEC storms. Our findings underscore the importance of refining microphysical parameterization schemes for accurate simulation of extreme rainfall events.

The influence of double-moment representation of warm-rain and ice hydrometeors on the numerical simulations of a mesoscale convective system (MCS) over the US Southern Great Plains has been evaluated. The Weather Research and Forecasting (WRF) model is used to simulate the MCS with three different microphysical schemes, including the WRF single-moment 6-class (WSM6), WRF double-moment 6-class (WDM6), and Morrison double-moment (MORR) schemes. It is found that the double-moment schemes outperform the single-moment schemes in terms of the simulated structure, life cycle, cloud coverage, precipitation, and microphysical properties of the MCS. However, compared with UND-Citation observations, collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), the WRF simulated ice hydrometeors with all three schemes do not agree well with the observations. Overall results from this study suggest that uncertainty in microphysical schemes could still be a productive area of future research from perspective of both model improvements and observations.

Physically based rainfall retrievals from passive microwave sensors often make use of cloud-resolving models (CRMs) to build a priori databases of potential rain structures. Each CRM, however, has its own cloud microphysics assumptions. Hence, approximated microphysics may cause uncertainties in the a priori information resulting in inaccurate rainfall estimates. This study first builds a priori databases by combining the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) observations and simulations from the Weather Research and Forecasting (WRF) model with six different cloud microphysics schemes. The microphysics schemes include the Purdue–Lin (LIN), WRF Single-Moment 6 (WSM6), Goddard Cumulus Ensemble (GCE), Thompson (THOM), WRF Double-Moment 6 (WDM6), and Morrison (MORR) schemes. As expected, the characteristics of the a priori databases are inherited from the individual cloud microphysics schemes. There are several distinct differences in the databases. Particularly, excessive graupel and snow exist with the LIN and THOM schemes, while more rainwater is incorporated into the a priori information with WDM6 than with any of the other schemes. Major results show that convective rainfall regions are not well captured by the LIN and THOM schemes-based retrievals. Rainfall distributions and their quantities retrieved from the WSM6 and WDM6 schemes-based estimations, however, show relatively better agreement with the PR observations. Based on the comparisons of the various microphysics schemes in the retrievals, it appears that differences in the a priori databases considerably affect the properties of rainfall estimations.

Investigation of riming within mixed-phase stratiform clouds using Weather Research and Forecasting (WRF) model

A field campaign to study cold season fog in complex terrain was conducted as a component of the Mountain Terrain Atmospheric Modeling and Observations (MATERHORN) Program from 07 January to 01 February 2015 in Salt Lake City and Heber City, Utah, United States. To support the field campaign, an advanced research version of the Weather Research and Forecasting (WRF) model was used to produce real-time forecasts and model evaluation. This paper summarizes the model performance and preliminary evaluation of the model against the observations. Results indicate that accurately forecasting fog is challenging for the WRF model, which produces large errors in the near-surface variables, such as relative humidity, temperature, and wind fields in the model forecasts. Specifically, compared with observations, the WRF model overpredicted fog events with extended duration in Salt Lake City because it produced higher moisture, lower wind speeds, and colder temperatures near the surface. In contrast, the WRF model missed all fog events in Heber City, as it reproduced lower moisture, higher wind speeds, and warmer temperatures against observations at the near-surface level. The inability of the model to produce proper levels of near-surface atmospheric conditions under fog conditions reflects uncertainties in model physical parameterizations, such as the surface layer, boundary layer, and microphysical schemes.

Dynamical downscaling is a widely used technique to properly capture regional surface heterogeneities that shape the local hydroclimatology. However, in the context of dynamical downscaling, the impacts on simulation fidelity have not been comprehensively evaluated across many user-specified factors, including the refinements of model horizontal resolution, large-scale forcing datasets, and dynamical cores. Two global-to-regional downscaling methods are used to assess these: specifically, the variable-resolution Community Earth System Model (VR-CESM) and the Weather Research and Forecasting (WRF) Model with horizontal resolutions of 28, 14, and 7 km. The modeling strategies are assessed by comparing the VR-CESM and WRF simulations with consistent physical parameterizations and grid domains. Two groups of WRF Models are driven by either the NCEP reanalysis dataset (WRF_NCEP) or VR-CESM7 results (WRF_VRCESM) to evaluate the effects of large-scale forcing datasets. The simulated hydroclimatologies are compared with reference datasets for key properties including total precipitation, snow cover, snow water equivalent (SWE), and surface temperature. The large-scale forcing datasets are critical to the WRF simulations of total precipitation but not surface temperature, controlled by the wind field and atmospheric moisture transport at the ocean boundary. No significant benefit is found in the regional average simulated hydroclimatology by increasing horizontal resolution refinement from 28 to 7 km, probably due to the systematic biases from the diagnostic treatment of rainfall and snowfall in the microphysics scheme. The choice of dynamical core has little impact on total precipitation but significantly determines simulated surface temperature, which is affected by the snow-albedo feedback in winter and soil moisture estimations in summer.

Moisture boundaries, or drylines, are common over the southern U.S. high plains and are one of the most important airmass boundaries for convective initiation over this region. In favorable environments, drylines can initiate storms that produce strong and violent tornadoes, large hail, lightning, and heavy rainfall. Despite their importance, there are few studies documenting climatological dryline location and frequency, or performing systematic dryline forecast evaluation, which likely stems from difficulties in objectively identifying drylines over large datasets. Previous studies have employed tedious manual identification procedures. This study aims to streamline dryline identification by developing an automated, multiparameter algorithm, which applies image-processing and pattern recognition techniques to various meteorological fields and their gradients to identify drylines. The algorithm is applied to five years of high-resolution 24-h forecasts from Weather Research and Forecasting (WRF) Model simulations valid April–June 2007–11. Manually identified dryline positions, which were available from a previous study using the same dataset, are used as truth to evaluate the algorithm performance. Generally, the algorithm performed very well. High probability of detection (POD) scores indicated that the majority of drylines were identified by the method. However, a relatively high false alarm ratio (FAR) was also found, indicating that a large number of nondryline features were also identified. Preliminary use of random forests (a machine learning technique) significantly decreased the FAR, while minimally impacting the POD. The algorithm lays the groundwork for applications including model evaluation and operational forecasting, and should enable efficient analysis of drylines from very large datasets.

This study is intended to simulate and better understand mean and extreme precipitation over the Loess Plateau (LP) in China using the Weather Research and Forecasting (WRF) model. We performed a long-term (1980–2011) simulation with the WRF model at a 10 km horizontal resolution, forced by the ERA-Interim reanalysis. A series of sensitivity experiments were conducted to investigate the influence of resolution and physical parameterization schemes on the simulation ability of the WRF model over the LP. The region has a dominant semi-arid climate and is adversely affected by extreme precipitation. Results show that the WRF model produced better simulations at a 10 km resolution for the LP with complex terrain than it did at coarser resolutions. WRF simulations for precipitation over the LP were most sensitive to cumulus schemes, moderately sensitive to planetary boundary layer schemes, and less sensitive to microphysics schemes. The WRF model not only adequately captured the spatial distribution of precipitation in the LP but also reproduced its magnitude and variability at different time scales. Moreover, the WRF model captured 73% of the observed extreme precipitation events with an intensity of more than 12 mm day− 1, which cause substantial soil erosion in the LP. Although it was unable to capture 27% of the observed events, the WRF model reasonably reproduced ERA-Interim extreme precipitation and its dynamic processes. These analyses strongly suggest that the biased extreme precipitation generated with the WRF model most likely resulted from inaccurate ERA-Interim forcing data. This study provides a better understanding of extreme precipitation simulations as well as insight into predictions of soil erosion in the LP.

The Lake Champlain Basin has socioeconomic and ecological significance for the Northeastern United States and Quebec, Canada. Temperatures and extreme precipitation events have been increasing across this region over the past three decades. Accurate, high-resolution climate simulations are critical to assessing potential climate change risk in the Lake Champlain Basin. We evaluate the performance of a regional climate model, the Weather Research and Forecasting (WRF) model, to downscale ERA-Interim reanalysis data to 4 km for the Lake Champlain Basin. Specifically, we compare an ensemble of five WRF experiments with different physics configurations using a one-way, triple-nested domain (36, 12, and 4 km) over three 5-year periods (1980–1984, 1995–1999, and 2010–2014) to Daymet, a gridded observational dataset. We find that WRF simulations of the Lake Champlain Basin generally reproduce the observed temperature and precipitation seasonal cycles, but have cold and wet biases. The simulation of mean temperature by WRF is most sensitive to the choice of radiation scheme, while the simulation of mean precipitation is most sensitive to the choice of radiation, cumulus, and microphysics scheme. We find that turning the cumulus scheme on improves the simulation of the precipitation seasonal cycle at a 4 km resolution, but also substantially enhances the wet bias. Using a coarser resolution (36 km) produces smaller regionally averaged precipitation biases, but not improved correlations between simulated and observed monthly precipitation. Both spatial resolution and turning the cumulus scheme off have minor effects on simulated temperature.

Abstract. Regions with high ice water content (HIWC), composed of mainly small ice crystals, frequently occur over convective clouds in the tropics. Such regions can have median mass diameters (MMDs) <300 µm and equivalent radar reflectivities <20 dBZ. To explore formation mechanisms for these HIWCs, high-resolution simulations of tropical convective clouds observed on 26 May 2015 during the High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) international field campaign based out of Cayenne, French Guiana, are conducted using the Weather Research and Forecasting (WRF) model with four different bulk microphysics schemes: the WRF single‐moment 6‐class microphysics scheme (WSM6), the Morrison scheme, and the Predicted Particle Properties (P3) scheme with one- and two-ice options. The simulations are evaluated against data from airborne radar and multiple cloud microphysics probes installed on the French Falcon 20 and Canadian National Research Council (NRC) Convair 580 sampling clouds at different heights. WRF simulations with different microphysics schemes generally reproduce the vertical profiles of temperature, dew-point temperature, and winds during this event compared with radiosonde data, and the coverage and evolution of this tropical convective system compared to satellite retrievals. All of the simulations overestimate the intensity and spatial extent of radar reflectivity by over 30 % above the melting layer compared to the airborne X-band radar reflectivity data. They also miss the peak of the observed ice number distribution function for 0.1<Dmax<1 mm. Even though the P3 scheme has a very different approach representing ice, it does not produce greatly different total condensed water content or better comparison to other observations in this tropical convective system. Mixed-phase microphysical processes at −10 ∘C are associated with the overprediction of liquid water content in the simulations with the Morrison and P3 schemes. The ice water content at −10 ∘C increases mainly due to the collection of liquid water by ice particles, which does not increase ice particle number but increases the mass/size of ice particles and contributes to greater simulated radar reflectivity.

The fire weather of south-east Australia from 1985 to 2009 has been simulated using the Weather Research and Forecasting (WRF) model. The US National Oceanic and Atmospheric Administration Centers for Environmental Prediction and National Center for Atmospheric Research reanalysis supplied the lateral boundary conditions and initial conditions. The model simulated climate and the reanalysis were evaluated against station-based observations of the McArthur Forest Fire Danger Index (FFDI) using probability density function skill scores, annual cumulative FFDI and days per year with FFDI above 50. WRF simulated the main features of the FFDI distribution and its spatial variation, with an overall positive bias. Errors in average FFDI were caused mostly by errors in the ability of WRF to simulate relative humidity. In contrast, errors in extreme FFDI values were driven mainly by WRF errors in wind speed simulation. However, in both cases the quality of the observed data is difficult to ascertain. WRF run with 50-km grid spacing did not consistently improve upon the reanalysis statistics. Decreasing the grid spacing to 10 km led to fire weather that was generally closer to observations than the reanalysis across the full range of evaluation metrics used here. This suggests it is a very useful tool for modelling fire weather over the entire landscape of south-east Australia.

<p>Superbolts are defined as lightning flashes that are a thousand times stronger than normal ones, and their occurrence is estimated to be less than 0.001% of total number of lightning on earth. The global distribution of these extremely powerful lightning flashes is remarkably different than that of regular lightning, which are concentrated in the well-known convective "chimneys" in tropical Africa, South-America and the maritime continent in South-East Asia. The physical mechanisms producing these powerful flashes remain unknown, and the puzzle is exacerbated by the fact that they are discovered mostly over oceans, in maritime winter storms.</p><p>The Mediterranean Sea is one of the most prolific regions where super-bolts occur, especially in the months November-January (Holzworth et al., 2019). We analyzed 8 years of lightning data obtained from the Israeli Lightning Detection Network (ILDN), defining a 200kA peak current threshold for superbolts. We mapped the spatial and temporal distribution of superbolts and their monthly frequency in winter season thunderstorms (DJF) in the eastern Mediterranean, and identified the meteorological and microphysical circumstances in such storms.</p><p>Our working hypothesis is that large amounts of desert dust aerosols, coming from the Sahara Desert, are ingested into maritime winter storms over the eastern Mediterranean. The large dust contributes to convective invigoration, enhanced freezing and efficient charge separation, implying that superbolts are more likely to occur in the presence of large dust. We will present the results of simulation conducted using the WRF-ELEC numerical model, and WRF with spectral bin microphysics coupled with Lynn et al.'s (2012) Dynamic Lightning Scheme (DLS) and the Lightning Potential Index (Yair et al., 2010; LPI), for selected case studies when an enhanced fraction of superbolts was observed.</p><p> </p><p>Holzworth, R. H., McCarthy, M. P., Brundell, J. B., Jacobson, A. R., and Rodger, C. J. (2019). Global distribution of superbolts. J. Geophys. Res. Atmos., 124., doi:10.1029/2019JD030975.</p><p>Lynn, B., Y. Yair, C. Price, G. Kelman and A. J. Clark (2012). Predicting cloud-to-ground and intracloud lightning in weather forecast models. Weather and Forecasting, 27, 1470-1488, doi:10.1175/WAF-D-11-00144.1.</p><p>Yair, Y., B. Lynn, C. Price, V. Kotroni, K. Lagouvardos, E. Morin, A. Mugnai, and M. d. C. Llasat (2010). Predicting the potential for lightning activity in Mediterranean storms based on the Weather Research and Forecasting (WRF) model dynamic and microphysical fields, J. Geophys. Res., 115, D04205, doi:10.1029/2008JD010868</p>