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With the advent of phased array radar (PAR) technology, it is possible to capture the development and evolution of convective systems in a much shorter time interval and with higher spatial resolution than traditional Doppler radar. Research on the assimilation of PAR observations in numerical weather prediction model is still very few in China. In this study, the impact of assimilating PAR data on the model forecasts is investigated with a case study of a local heavy rainfall system that occurred in Foshan city, Guangdong Province on 26 August 2020. A series of sensitive experiments for this study is conducted. Both of the retrieved three-dimensional wind and hydrometeor fields are assimilated through nudging method for the TRAMS_RUC_1km (Tropical Regional Assimilation Model for South China sea_The Rapid Update Cycle_1km). The temperature and moist fields are also adjusted accordingly. The results show that significant improvements are made by the experiments with latent heat nudging and adjustment of moisture vapor field, which implies the importance of thermodynamic balance in the initialization of convective system and highlights the need to assimilate PAR radar observation in a continuous manner to maximize the impact of the data. Sensitivity tests also indicate that the relaxation time should be less than 5 minutes. In general, the assimilation of PAR data can significantly improve the nowcasting of regional heavy precipitation in this case. This paper is the first step toward operational PAR data assimilation in the numerical weather prediction model of southern China.

The goal of this work is to assess the sensitivity of the Weather Research and Forecasting (WRF) model to various microphysical and land surface model parameterizations, as well as their effectiveness in capturing a radiation fog event that occurred on 15–16 February 2000 over Delhi in the Indo-Gangetic Plain (IGP). Fog forecasting using state-of-the-art mesoscale models is challenging due to limitations in understanding of atmosphere-land surface feedbacks and fog microphysics. Fog can have adverse effects because of low visibility on transport and aviation but affects positively agriculture and forestry by absorbing fog water. In India, the IGP is particularly susceptible to fog during the winter months of December, January, and February (DJF). To reach the goal in this work, preliminary investigation is carried out with five model experiments centered at Delhi for testing model sensitivity to nesting and gravitational settling of fog. The non-nested domain fares better at fog forecasting as compared to the nested domain. Accounting for the gravitational settling of fog droplets in the model further enhances model performance. Thereafter, to assess the model sensitivity to parameterization schemes, 40 model suite combinations with five microphysical (MP) schemes, two planetary boundary layer (PBL) schemes, and four land surface model (LSM) schemes are compared. The validations for fog formation, development, and dissipation are performed using observations collected over Safdarjung airport in Delhi. We conclude that the Thompson MP scheme, along with the Mellor-Yamada Nakanishi-Niino (MYNN 2.5) PBL and Rapid Update Cycle (RUC) LSM scheme, is able to capture the radiation fog event better than other schemes, and it is critical for evaluating the life cycle of radiation fog.

Initialization of tropical cyclones plays an important role in typhoon numerical prediction. This study applied a typhoon initialization scheme based on the incremental analysis updates (IAU) technique in a rapid refresh system to improve the prediction of Typhoon Lekima (2019). Two numerical sensitivity experiments with or without application of the IAU technique after performing vortex relocation and wind adjustment procedures were conducted for comparison with the control experiment, which did not involve a typhoon initialization scheme. Analysis of the initial fields indicated that the relocation procedure shifted the typhoon circulation to the observed typhoon region, and the wind speeds became closer to the observations following the wind adjustment procedure. Comparison of the results of the sensitivity and control experiments revealed that the vortex relocation and wind adjustment procedures could improve the prediction of typhoon track and intensity in the first 6-h period, and that these improvements were extended throughout the first 12-h period of the prediction by the IAU technique. The new typhoon initialization scheme also improved the simulated typhoon structure in terms of not only the wind speed and warm core prediction but also the organization of the eye of Typhoon Lekima. Diagnosis of the tendencies of variables showed that use of the IAU technique in a typhoon initialization scheme is efficacious in resolving the spurious high-frequency noise problem such that the model is able to reach equilibrium as soon as possible.

The Weather Research and Forecasting (WRF) model was employed to simulate the water and energy process over the near surface in the Yellow River source region from July to September 2015. The Rapid Update Cycle (RUC), Noah, Noah-Multi-Physics (Noah-MP), and Community Land Model (CLM) version 4 land surface schemes were selected and evaluated in our experiments. The simulated results were also compared with the Global Land Data Assimilation System (GLDAS) outputs, the ground-based automatic weather stations (AWSs), and the Zoige plateau wetlands ecosystem research flux station data. There was substantial agreement between the results of the simulations and observations in terms of 2-m temperature and relative humidity. However, the simulated values of the 2-m temperature were mostly lower than observations, while the values of relative humidity were higher than the observations. The correlation coefficients (R) between the simulations and observations of air temperature in four experiments were all higher than 0.94, and simulated results of the WRF-RUC experiment had the minimum bias compared with other experiments. The root mean square error (RMSE) and bias of 2-m relative humidity between the WRF-CLM4 simulations and observations were lower than other experiments. Moreover, the simulation of the 10-cm soil temperature was lower than the observations. WRF-CLM4 simulations agreed better with the observations, and WRF-Noah-MP simulations had the largest bias in all of the experiments. The soil moisture increased rapidly when precipitation was occurring and decreased slowly after the precipitation. The four experiments overestimate precipitation leading to higher soil moisture compared with the real situations. The peak value of net radiation was appropriately 850 W/m2 during the summertime in the Zoige plateau area. The simulated net radiation agreed well with the observation except in the WRF-RUC scheme. During the summertime, vegetation grew rapidly, and the latent heat flux transport increased and played a dominant role in surface energy transport in the Zoige area. The RMSE between the simulation and observation of latent heat flux had the minimum value of 98.0 W/m2 in the WRF-CLM4 scheme, and the bias of sensible heat flux had the minimum value of 15.4 W/m2 in the WRF-Noah scheme. This study used the WRF model to explore the applicability of the different land surface schemes in the Zoige plateau area. The research related to this study can be used to understand the water-energy cycle process over the Qinghai-Tibet Plateau wetland and can also provide references for the application of regional meteorological models over this area.

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

The Great Plains low-level jet (LLJ) is a contributing factor to the initiation and evolution of nocturnal Mesoscale Convective Systems (MCSs) in the central United States by supplying moisture, warm air advection, and a source of convergence. Thus, the ability of models to correctly depict thermodynamics in the LLJ likely influences how accurately they forecast MCSs. In this study, the Weather Research and Forecasting (WRF) model was used to examine the relationship between spatial displacement errors for initiating simulated MCSs, and errors in forecast thermodynamic variables up to three hours before downstream MCS initiation in 18 cases. Rapid Update Cycle (RUC) analyses in 3 layers below 1500 m above ground level were used to represent observations. Correlations between simulated MCS initiation spatial displacements and errors in the magnitude of forecast thermodynamic variables were examined in regions near and upstream of both observed and simulated MCSs, and were found to vary depending on the synoptic environment. In strongly-forced cases, large negative moisture errors resulted in simulated MCSs initiating further downstream with respect to the low-level flow from those observed. For weakly-forced cases, correlations were weaker, with a tendency for smaller negative moisture errors to be associated with larger displacement errors to the right of the inflow direction for initiating MCSs.

The ability of the Weather Research and Forecasting (WRF) model to simulate multilevel soil moisture (SM), 2-m air temperature (T2m), and 2-m relative humidity (RH2m) was evaluated for five different locations in India. WRF model simulations were carried out for 30 cases during different seasons with two different land surface schemes, viz. Noah and Rapid Update Cycle (RUC). The simulations were compared with in situ observations taken routinely at 30-min time intervals at the five selected locations. Statistical evaluation showed that, although the model could simulate SM reasonably well [with the majority of cases falling in the 50% RE category) for Almora (ALR), Hyderabad (HYD), and Cochin (CHN). In case of T2m, model errors were high (RE > 15%) over hilly terrain, e.g., at ALR, while errors were relatively lower (RE < 10%) for plane areas such as HYD, GLB, DLH, and CHN. In general, the diurnal variation showed that the model underestimated (overestimated) afternoon temperatures during nonrainy (rainy) days. RH2m was also well simulated by the model at the locations HYD, GLB, and CHN, although it underestimated RH2m during morning hours at the locations ALR and DLH. Overall, the comparison showed that the WRF model could reproduce the near-surface temperature and humidity for plane areas such as HYD, GLB, and CHN reasonably well, but has limitations for complex terrains, e.g., at ALR, and highly polluted cities such as DLH.

The Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

Assessment of the land surface models (LSMs) on monsoon studies over the Indian summer monsoon (ISM) region is essential. In this study, we evaluate the skill of LSMs at 10 km spatial resolution in simulating the 2010 monsoon season. The thermal diffusion scheme (TDS), rapid update cycle (RUC), and Noah and Noah with multi-parameterization (Noah-MP) LSMs are chosen based on nature of complexity, that is, from simple slab model to multi-parameterization options coupled with the Weather Research and Forecasting (WRF) model. Model results are compared with the available in situ observations and reanalysis fields. The sensitivity of monsoon elements, surface characteristics, and vertical structures to different LSMs is discussed. Our results reveal that the monsoon features are reproduced by WRF model with all LSMs, but with some regional discrepancies. The model simulations with selected LSMs are able to reproduce the broad rainfall patterns, orography-induced rainfall over the Himalayan region, and dry zone over the southern tip of India. The unrealistic precipitation pattern over the equatorial western Indian Ocean is simulated by WRF–LSM-based experiments. The spatial and temporal distributions of top 2-m soil characteristics (soil temperature and soil moisture) are well represented in RUC and Noah-MP LSM-based experiments during the ISM. Results show that the WRF simulations with RUC, Noah, and Noah-MP LSM-based experiments significantly improved the skill of 2-m temperature and moisture compared to TDS (chosen as a base) LSM-based experiments. Furthermore, the simulations with Noah, RUC, and Noah-MP LSMs exhibit minimum error in thermodynamics fields. In case of surface wind speed, TDS LSM performed better compared to other LSM experiments. A significant improvement is noticeable in simulating rainfall by WRF model with Noah, RUC, and Noah-MP LSMs over TDS LSM. Thus, this study emphasis the importance of choosing/improving LSMs for simulating the ISM phenomena in a regional model.

Weather Research and Forecasting (WRF) is an open source numerical weather prediction model that can be used for high resolution rainfall predictions. Besides these advantages, WRF output accuracy can be affected by the initial condition. The accuration of WRF model can be improved by data assimilation. Data assimilation is combining observation data with model data to improve the initial state of atmospheric flow. This study aims to investigate the effect of assimilation weather radar in models using WRF for predictions rainfall events in Palembang region on November 12th, 2018. This study uses radar radial velocity data as input data for assimilation. The assimilation technique uses the 3DVAR with rapid update cycle (RUC) procedure 1 hour, 3 hours, 6 hours with spin up 12 and 6 hours. The output of the model verified using Global Satellite Mapping of Precipitation (GSMaP) data and using rain gauge data for point verification. The results of this study indicate that the output of the assimilation model, especially in the spin-up 12 hours skenario implementation of the 1-hour RUC is better than the model without assimilation. From the eight scenario models implemented, it can be concluded that the 12 hours spin up is better than the 6 hours spin up.

Simulations are widely used to calculate the thermal environment and energy consumption of buildings. The results of these simulations are affected by weather data, thus making the selection of appropriate weather data essential. Typical weather data are used to analyze a building’s thermal performance, but this is not appropriate for analyzing the performance of an actual building controlled by a building energy management system, which responds to real-time weather conditions. Such a building needs short-term weather forecast data to calculate the energy consumption. However, an evaluation of the validity of weather forecast data has not been performed. This study quantitatively analyzes the validation of real-time weather forecast data. A middleware platform was developed to combine weather forecast data and the EnergyPlus software using Grasshopper software. In addition, weather forecast data and actual weather data were compared to evaluate the forecast validation and a predictive control method using weather forecast data was devised. The application of real-time control was found to reduce the electricity costs incurred for cooling by 10% relative to there being no control, and by 2% relative to fixed temperature control.

The likely spread of the current spruce budworm (SBW; Choristoneura fumiferana [Clem.]) outbreak fromhigh to low density areas brings to the forefront a pressing need to understand its dispersal dynamics and to document mass exodus flights in relation to weather patterns. In this study, we used the weather surveillance radar of Val d'Irene in eastern Canada in combination with weather information from the Rapid Update Cycle (RUC) model output to track and document a SBW mass exodus flight that occurredon July 15-16th 2013. Analyses confirmed the potential of using weather radar and RUC data to help assess SBW mass exodus dynamics. Weather surveillance radar data suggested that the mass exodus flight originated from both the northern and southern sides of the St- Lawrence River estuary with mostindividuals originating from severely defoliated areas on the north shore. During the exodus flight, SBW moths may have covered a distance of over 200 km. Detailed large-scale assessment of this mass exodus flight using radar data allowed for the identification of convergence zones and a liftoff from a lightly defoliated area which has never been documented before. Based on radar and lower tropospheric weather data, SBW dispersed downwind in a rather shallow layer, probably between 400 and 800 m. These results imply that moths were generally dispersing in the vicinity of the top of the temperature inversion zone where both temperature and wind were highest throughout the exodus flight period. We advocate that the use of weather radar technology coupled with data on lower tropospheric weather conditions might benefit other monitoring tools already being used and may also help calibrate SBW atmospheric transport models.

A heavy rainfall event that occurred in Beijing on 7 August 2015 was investigated by means of several nonconventional observational data and the Beijing Rapid Update Cycle Model (v3.0). A cold front guided by an upper trough and the wind shear line are main factors influencing this rainfall event. The two factors combined were enhanced after passing the Beijing downtown area. A Nourban land use scenario was employed to analyze the impact of urbanization on this rainfall event. The results show that the urban land use type, which induces the wind shear line, is indispensable in forecasting the rainstorm. The urban land use type leads to gradients of the sensible heat flux, temperature, and wind increasing in the suburban area, which may allow the synoptic systems to merge and enhance. Urbanization caused the precipitation to increase in the downstream suburban area and the spatial distribution of precipitation to become more concentrated. Ensemble tests of different parameterization schemes confirm that the results are reliable.

Three atmospheric boundary layer (ABL) schemes and two land surface models that are used in the Weather Research and Forecasting (WRF) model, version 3.4.1, were evaluated with numerical simulations by using data from the north coast of France (Dunkerque). The ABL schemes YSU (Yonsei University), ACM2 (Asymmetric Convective Model version 2), and MYJ (Mellor–Yamada–Janjic) were combined with two land surface models, Noah and RUC (Rapid Update Cycle), in order to determine the performances under sea-breeze conditions. Particular attention is given in the determination of the thermal internal boundary layer (TIBL), which is very important in air pollution scenarios. The other physics parameterizations used in the model were consistent for all simulations. The predictions of the sea-breeze dynamics output from the WRF model were compared with observations taken from sonic detection and ranging, light detection and ranging systems and a meteorological surface station to verify that the model had reasonable accuracy in predicting the behavior of local circulations. The temporal comparisons of the vertical and horizontal wind speeds and wind directions predicted by the WRF model showed that all runs detected the passage of the sea-breeze front. However, except for the combination of MYJ and Noah, all runs had a time delay compared with the frontal passage measured by the instruments. The proposed study shows that the synoptic wind attenuated the intensity and penetration of the sea breeze. This provided changes in the vertical mixing in a short period of time and on soil temperature that could not be detected by the WRF model simulations with the computational grid used. Additionally, among the tested schemes, the combination of the localclosure MYJ scheme with the land surface Noah scheme was able to produce the most accurate ABL height compared with observations, and it was also able to capture the TIBL.

In the present study, the physical parameterizations of the Weather Research and Forecasting (WRF) model are verified for making accurate inferences about the dynamics of the Thermal Internal Boundary Layer (TIBL) generated by sea breeze in an urban center with an island in a bay along a coastal region with rugged topography. The simulations were performed using parameterizations from Yonsei University (YSU), Mellor-Yamada-Janjic (MYJ) and Asymmetric Convective Model version 2 (ACM2) for the atmospheric boundary layer (ABL) and Noah and Rapid Update Cycle (RUC) for the Land Surface Model (LSM). The data inferred by the WRF model were compared with those obtained by a Surface Meteorological Station (SMS) and by measurements generated using Light Detection and Ranging (LIDAR), Sonic Detection and Ranging (SODAR) and radiosonde. The simulations showed that although the object of this research was a region with high geographical complexity, the YSU parameterization set (non-local closure) for the ABL and the Noah parameterization for the LSM presented satisfactory results in determining ABL height generated by the sea breeze on the day in question.

Since November 2014, the Meteorological Services of Canada (MSC) has been running a real-time numerical weather prediction system that provides deterministic forecasts on a regional domain with a 2.5-km horizontal grid spacing covering a large portion of Canada using the Global Environmental Multiscale (GEM) forecast model. This system, referred to as the High Resolution Deterministic Prediction System (HRDPS), is currently downscaled from MSC’s operational 10-km GEM-based regional system but uses initial surface fields from a high-resolution (2.5 km) land data assimilation system coupled to the HRDPS and initial hydrometeor fields from the forecast of a 2.5-km cycle, which reduces the spinup time for clouds and precipitation. Forecast runs of 48 h are provided four times daily. The HRDPS was tested and compared to the operational 10-km system. Model runs from the two systems were evaluated against surface observations for common weather elements (temperature, humidity, winds, and precipitation), fractional cloud cover, and also against upper-air soundings, all using standard metrics. Although the predictions of some fields were degraded in some specific regions, the HRDPS generally outperformed the operational system for a majority of the scores. The evaluation illustrates the added value of the 2.5-km model and the potential for improved numerical guidance for the prediction of high-impact weather.

Variability of large and synoptic scale circulations in Asia is strongly affected by the winter and spring Eurasian snow. Therefore, an accurate prediction of the Eurasian snow is of the utmost importance in predicting the climate and weather phenomena in Asia. Most global/regional models are coupled with several land surface models (LSMs) in which the land surface process parameters are calculated under their own physical principles and parameterization schemes. In this study, using the Weather Research and Forecasting (WRF) model, we make intercomparision of LSMs in terms of simulating the Eurasian snow. Simulations are carried out from 1 June 2009 to 31 August 2010, including a spin-up time of 6 months, by employing four different LSMs – the Unified Noah LSM, the Noah LSM with multiparameterization options (Noah-MP), the Rapid Update Cycle (RUC) LSM, and the Community Land Model version 4 (CLM4). The NCEP Final (FNL) Operational Global Analysis data are used as initial and boundary conditions. The LSM results are evaluated using the Canadian Meteorological Centre Daily Snow Depth Analysis Data, the Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra Snow Cover Monthly L3 Global 0.05Deg Climte Modeling Grid (CMG) Version 6, and the MODIS Bidirectional Reflectance Distribution Function (BRDF)/Albedo Product. Although all the LSMs represent reasonable results, the Noah-MP represents the most accurate predictions in all three variables (snow depth, fractional snow cover, and albedo), in terms of not only quantitative aspects but also spatial correlation patterns. Our results indicate that prediction of the Eurasian snow cover is sensitive to the choice of LSMs coupled to the global/regional climate models, and hence the future climate projections.

The impact of assimilating lower–tropospheric lidar temperature profiles into a numerical weather prediction (NWP) model was investigated. The profiles were measured with the Temperature Rotational Raman Lidar (TRRL) of the University of Hohenheim on 24 April 2013. The day showed the development of a typical daytime planetary boundary layer (PBL) with no optically thick clouds. The Weather Research and Forecasting model was operated with 57 vertical levels covering Central Europe with 3 km horizontal resolution. Three different experiments were carried out with a rapid update cycle with hourly three–dimensional variational data assimilation. The impact run (ALL_DA) was performed with the assimilation of conventional data and the additional assimilation of TRRL profiles between 0900 and 1800 UTC in a height range from about 500 m to 3000 m above ground level with a vertical resolution of about 100 m. In CONV_DA and NO_DA, only conventional data and no data were assimilated, respectively. To consider the representativeness of the TRRL profiles, an observation error of 0.7 K was used for all heights. The assimilation was performed using the radiosonde operator. The TRRL data assimilation corrected the temperature profiles towards the lidar data. In the mean, the boundary layer height was improved by 60 m in ALL_DA compared to the TRRL data and the temperature gradient in the entrainment layer by 0.19 K (100 m)− 1. While ALL_DA showed a root mean square error (RMSE) of 0.6 K compared to the TRRL data, the RMSE of CONV_DA was twice as large. Compared to data from radiosondes launched at the TRRL site, ALL_DA showed a significantly smaller RMSE than CONV_DA in two out of four times radiosonde data were available. We conclude that the assimilation of TRRL data has great potential to close the critical gap of missing temperature observations in the lower troposphere.

The land surface model (LSM) described in this manuscript was originally developed as part of the NOAA Rapid Update Cycle (RUC) model development effort; with ongoing modifications, it is now used as an option for the WRF community model. The RUC model and its WRF-based NOAA successor, the Rapid Refresh (RAP), are hourly updated and have an emphasis on short-range, near-surface forecasts including aviation-impact variables and preconvective environment. Therefore, coupling to this LSM (hereafter the RUC LSM) has been critical to provide more accurate lower boundary conditions. This paper describes changes made to the RUC LSM since earlier descriptions, including extension from six to nine levels, improved snow treatment, and new land-use data from MODIS.The RUC LSM became operational at the NOAA/National Centers for Environmental Prediction (NCEP) as part of the RUC from 1998–2012 and as part of the RAP from 2012 through the present. The simple treatments of basic land surface processes in the RUC LSM have proven to be physically robust and capable of realistically representing the evolution of soil moisture, soil temperature, and snow in cycled models. Extension of the RAP domain to encompass all of North America and adjacent high-latitude ocean areas necessitated further development of the RUC LSM for application in the tundra permafrost regions and over Arctic sea ice. Other modifications include refinements in the snow model and a more accurate specification of albedo, roughness length, and other surface properties. These recent modifications in the RUC LSM are described and evaluated in this paper.

The Rapid Refresh (RAP), an hourly updated assimilation and model forecast system, replaced the Rapid Update Cycle (RUC) as an operational regional analysis and forecast system among the suite of models at the NOAA/National Centers for Environmental Prediction (NCEP) in 2012. The need for an effective hourly updated assimilation and modeling system for the United States for situational awareness and related decision-making has continued to increase for various applications including aviation (and transportation in general), severe weather, and energy. The RAP is distinct from the previous RUC in three primary aspects: a larger geographical domain (covering North America), use of the community-based Advanced Research version of the Weather Research and Forecasting (WRF) Model (ARW) replacing the RUC forecast model, and use of the Gridpoint Statistical Interpolation analysis system (GSI) instead of the RUC three-dimensional variational data assimilation (3DVar). As part of the RAP development, modifications have been made to the community ARW model (especially in model physics) and GSI assimilation systems, some based on previous model and assimilation design innovations developed initially with the RUC. Upper-air comparison is included for forecast verification against both rawinsondes and aircraft reports, the latter allowing hourly verification. In general, the RAP produces superior forecasts to those from the RUC, and its skill has continued to increase from 2012 up to RAP version 3 as of 2015. In addition, the RAP can improve on persistence forecasts for the 1–3-h forecast range for surface, upper-air, and ceiling forecasts.

This study presents a performance evaluation of four different land surface models (LSM) available in Weather Forecast Research (WRF). The research site was located in Haean Basin in South Korea. The basin is very unique by its geomorphology and topography. For a better representation of the complex terrain in the mesoscale model were used a high resolution topography data with a spatial resolution of 30 meters. Additionally, land-use layer was corrected by ground mapping data-sets. The observation equipments used in the study were an ultrasonic anemometer with a gas analyzer, an automatic weather station and a tethered balloon sonde. The model simulation covers a four-day period during autumn. The result shows significant impact of LSM on meteorological simulation. The best agreement between observation and simulation was found in the case of WRF with Noah LSM (WRF-Noah). The WRF with Rapid Update Cycle LSM (WRF-RUC) has a very good agreement with temperature profiles due to successfully predicted fog which appeared during measurements and affected the radiation budget at the basin floor. The WRF with Pleim and Xiu LSM (WRF-PX) and WRF with Thermal Diffusion LSM (WRF-TD) performed insufficiently for simulation of heat fluxes. Both overestimated the sensible and underestimated the latent heat fluxes during the daytime.

To quantify and explain effects of different land surface schemes (LSSs) on simulated geopotential height (GPH) fields, we performed simulations over China for the summer of 2003 using 12-member ensembles with the Weather Research and Forecasting (WRF) Model, version 3. The results show that while the model can generally simulate the seasonal and monthly mean GPH patterns, the effects of the LSS choice on simulated GPH fields are substantial, with the LSS-induced differences exceeding 10 gpm over a large area (especially the northwest) of China, which is very large compared with climate anomalies and forecast errors. In terms of the assessment measures for the four LSS ensembles [namely, the five-layer thermal diffusion scheme (SLAB), the Noah LSS (NOAH), the Rapid Update Cycle LSS (RUC), and the Pleim–Xiu LSS (PLEX)] in the WRF, the PLEX ensemble is the best, followed by the NOAH, RUC, and SLAB ensembles. The sensitivity of the simulated 850-hPa GPH is more significant than that of the 500-hPa GPH, with the 500-hPa GPH difference fields generally characterized by two large areas with opposite signs due to the smoothly varying nature of GPHs. LSS-induced GPH sensitivity is found to be higher than the GPH sensitivity induced by atmospheric boundary layer schemes. Moreover, theoretical analyses show that the LSS-induced GPH sensitivity is mainly caused by changes in surface fluxes (in particular, sensible heat flux), which further modify atmospheric temperature and pressure fields. The temperature and pressure fields generally have opposite contributions to changes in the GPH. This study emphasizes the importance of choosing and improving LSSs for simulating seasonal and monthly GPHs using regional climate models.

An often used assumption in air pollution studies is a well-mixed boundary layer (BL), where pollutants are evenly distributed. Because of the difficulty in obtaining vertically resolved measurements, the validity of the assumption has not been thoroughly evaluated. In this study, we usemore than 200 vertical profiles observed in the Deriving Information on Surface Conditions from Column and Vertically Resolved Observations Relevant to Air Quality (DISCOVER-AQ) aircraft campaign in July 2011 to examine the vertical distributions of pollutants over the Washington-Baltimore area. While many long-lived species are well mixed in daytime, the observed average vertical profile of NOx shows a large negative gradient with increasing altitude in the BL. Our analysis suggests that the magnitude of the NOx gradient is highly sensitive to atmospheric stability. We investigate how parameterizations of the BL and land-surface processes impact vertical profiles in a 1-D chemical transport model, using three BL schemes (Asymmetric Convective Model version 2 (ACM2), Yonsei University (YSU), and Mellor-Yamada-Janjic (MYJ)) and two land-surface schemes (Noah and Rapid Update Cycle (RUC)). The model reasonably reproduces the median vertical profiles of NOx under different BL stability conditions within 30% of observations, classified based on potential temperature gradient and BL height. Comparisons with NOx observations for individual vertical profiles reveal that while YSU performs better in the turbulent and deep BL case, in general, ACM2 (RMSE=2.0ppbv) outperforms YSU (RMSE=2.5ppbv) and MYJ (RMSE=2.2ppbv). Results also indicate that the land-surface schemes in the Weather Research and Forecasting (WRF) model have a small impact on the NOx gradient. Usingmodel simulations, we analyze the impact of BL NOx gradient on the calculation of the ozone production rate and satellite NO2 retrieval. We show that using surface measurements and the well-mixed BL assumption causes a~45%highbias in the estimated BL ozoneproduction rate and that the variability of NO2 vertical profiles is responsible for 5–10% variability in the retrieved NO2 tropospheric vertical columns.

Recently, two analog-based postprocessing methods were demonstrated to reduce the systematic and random errors from Weather Research and Forecasting (WRF) Model predictions of 10-m wind speed over the central United States. To test robustness and generality, and to gain a deeper understanding of postprocessing forecasts with analogs, this paper expands upon that work by applying both analog methods to surface stations evenly distributed across the conterminous United States over a 1-yr period. The Global Forecast System (GFS), North American Mesoscale Forecast System (NAM), and Rapid Update Cycle (RUC) forecasts for screen-height wind, temperature, and humidity are postprocessed with the two analog-based methods and with two time series–based methods—a running mean bias correction and an algorithm inspired by the Kalman filter. Forecasts are evaluated according to a range of metrics, including random and systematic error components; correlation; and by conditioning the error distributions on lead time, location, error magnitude, and day-to-day error variability.Results show that the analog methods are generally more effective than time series–based methods at reducing the random error component, leading to an overall reduction in root-mean-square error. Details among the methods differ and are elucidated upon in this study. The relative levels of random and systematic error in the raw forecasts determine, to a large extent, the effectiveness of each postprocessing method in reducing forecast errors. When the errors are dominated by random errors (e.g., where thunderstorms are common), the analog-based methods far outperform the time series–based methods. When the errors are strictly systematic (i.e., a bias), the analog methods lose their advantage over the time series methods. It is shown that slowly evolving systematic errors rarely dominate, so reducing the random error component is most effective at reducing the error magnitude. The results are shown to be valid for all seasons. The analog methods show similar performance to the operational model output statistics (MOS) while showing greater reduction of random errors at certain lead times.

Traditional pixel-versus-pixel forecast evaluation scores such as the critical success index (CSI) provide a simple way to compare the performances of different forecasts; however, they offer little information on how to improve a particular forecast. This paper strives to demonstrate what additional information an object-based forecast evaluation tool such as the Method for Object-Based Diagnostic Evaluation (MODE) can provide in terms of assessing numerical weather prediction models’ convective storm forecasts. Forecast storm attributes evaluated by MODE in this paper include storm size, intensity, orientation, aspect ratio, complexity, and number of storms. Three weeks of the High Resolution Rapid Refresh (HRRR) model’s precipitation forecasts during the summer of 2010 over the eastern two-thirds of the contiguous United States were evaluated as an example to demonstrate the methodology. It is found that the HRRR model was able to forecast convective storm characteristics rather well either as a function of time of day or as a function of storm size, although significant bias does exist, especially in terms of storm number and storm size. Another interesting finding is that the model’s ability of forecasting new storm initiation varies substantially by regions, probably as a result of its different skills in forecasting convection driven by different forcing mechanisms (i.e., diurnal heating vs synoptic-scale frontal systems).

The GCSS working group on cirrus focuses on an inter-comparison of model simulations ranging from very detailed microphysical and dynamical models through to general circulation models (GCMs). The past GCSS cirrus cloud inter-comparison highlighted the wide range in modelling results that was a surprise to the modelling community. That inter-comparison was idealised and, therefore, a key issue was that it did not benefit from observations to help distinguish between model performances. In this work, we aim to address this key issue by developing an observationally based case study to be used for the GCSS cirrus modelling inter-comparison study. We focused on developing a case that had sufficient observations with which to evaluate models, to help identify which models in the inter-comparison are performing well and highlight areas for model development. Furthermore, it will provide a base case for future model comparisons or testing of new or updated models. This paper outlines the modelling case development and the inter-comparison results will be presented in a follow-on paper. The case was based on the 9 March 2000 Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) during an intensive observation period (IOP). The case was developed utilising various observations including ARM SGP remote sensing including the MilliMeter Cloud Radar (MMCR), radiometers, radiosondes, aircraft observations, satellite observations, objective analysis and complemented with results from the Rapid Update Cycle (RUC) model as well as bespoke gravity wave simulations used to provide the best estimate for large scale forcing. The retrievals of ice water content, ice number concentration and fall velocity provide several constraints to evaluate model performances. Initial testing of the case has been reported using the UK Met Office Large Eddy Simulation Model (LEM) which suggests the case is appropriate for the model inter-comparison study. To our knowledge, this case offers the most detailed case study for cirrus comparison available and we anticipate this will offer significant benefits over past comparisons which have mostly been loosely based on observations.

The forecast in areas of very complex topography, as for instance the Alpine region, is still a challenge even for the new generation of numerical weather prediction models which aim at reaching the km-scale. The problem is enhanced by a general lack of standard observations, which is even more evident over the southern side of the Alps. For this reason, it would be useful to increase the performance of the mathematical models by locally assimilating non-conventional data. Since in ARPA Piemonte there is the availability of a great number of non-GTS stations, it has been decided to assimilate the 2 m temperature, coming from this dataset, in the very-high resolution version of the COSMO model, which has a horizontal resolution of about 3 km, more similar to the average resolution of the thermometers. Four different weather situations have been considered, ranging from spring to winter, from cloudy to clear sky. The aim of the work is to investigate the effects of the assimilation of non-GTS data in order to create an operational very high-resolution analysis, but also to test the option of running in the future a very short-range forecast starting from these analyses (RUC or Rapid Update Cycle). The results, in terms of Root Mean Square Error, Mean Error and diurnal cycle of some surface variables such as 2 m temperature, 2 m relative humidity and 10 m wind intensity show a positive impact during the assimilation cycle which tends to dissipate a few hours after the end of it. Moreover, the 2 m temperature assimilation has a slightly positive or neutral impact on the vertical profiles of temperature, eventhough some calibration is needed for the precipitation field which is too much perturbed during the assimilation cycle, while it is unaffected in the forecast period. So the stability of the planetary boundary layer, on the one hand, has not been particularly improved by the new-data assimilation, but, on the other hand, it has not been destroyed. It has to be pointed out that a correct description of the planetary boundary layer, even only the lowest part of it, could be helpful to the forecasters and, in general, to the users, in order to deal with meteorological hazards such as snow (in particular snow/rain limit definition), or fog (description of temperature inversions).

Predictions of accurate heavy rain are needed in building a flood early warning system. One of the most commonly used weather parameter modeling models is the Weather Research Forecasting, but the results of the WRF model prediction have deficiencies inaccuracy so that data assimilation needs to be done to improve accuracy. This research aims to determine the effect of radar data assimilation by applying RUC using WRF 3DVAR to improve the predictions of heavy rain events in the Jabodetabek area with cases representing each of the four seasons, on February 20, 2017, April 3, 2017, June 13, 2017, and November 09, 2017. The data used for this research are synoptic observation data, GSMaP, GFS, Radar data, in the form of Z CAPPI products. In general, WRF radar data assimilation with RUC shows better spatial and point values. This can be seen in the spatial rainfall distribution on February 20th, 2017, June 13th, 2017, and November 09th, 2017 analysis on Climatology Station of PondokBetung, as well as the analysis of rain dichotomy, shows WRF assimilation using RUC with a TS value increased by 9%, PC value increased by 10%, and FAR value fixed by 18%.

The Weather Research and Forecasting (WRF) Model has been designed to be an efficient and flexible simulation system for use across a broad range of weather-forecast and idealized-research applications. Of particular interest is the use of WRF in nonhydrostatic applications in which moist-convective processes are treated explicitly, thereby avoiding the ambiguities of cumulus parameterization. To evaluate the capabilities of WRF for convection-resolving applications, real-time forecasting experiments have been conducted with 4 km horizontal mesh spacing for both convective systems in the central U.S. and for hurricanes approaching landfall in the southeastern U.S. These forecasts demonstrate a good potential for improving the forecast accuracy of the timing and location of these systems, as well as providing more detailed information on their structure and evolution that is not available in current coarser resolution operational forecast models.

This study extends the heated condensation framework (HCF) presented in Tawfik and Dirmeyer to include variables for describing the convective background state of the atmosphere used to quantify the contribution of the atmosphere to convective initiation within the context of land–atmosphere coupling. In particular, the ability for the full suite of HCF variables to 1) quantify the amount of latent and sensible heat energy necessary for convective initiation, 2) identify the transition from moistening advantage to boundary layer growth advantage, 3) identify locally originating convection, and 4) compare models and observations, directly highlighting biases in the convective state, is demonstrated. These capabilities are illustrated for a clear-sky and convectively active day over the Atmospheric Radiation Measurement Program Southern Great Plains central station using observations, the Rapid Update Cycle (RUC) operational model, and the North American Regional Reanalysis (NARR). The clear-sky day had a higher and unattainable convective threshold, making convective initiation unlikely. The convectively active day had a lower threshold that was attained by midafternoon, reflecting local convective triggering. Compared to observations, RUC tended to have the most difficulty representing the convective state and captured the threshold for the clear-sky case only because of compensating biases in the moisture and temperature profiles. Despite capturing the observed moisture profile very well, a stronger surface inversion in NARR returned overestimates in the convective threshold. The companion paper applies the HCF variables introduced here across the continental United States to examine the climatological behavior of convective initiation and local land–atmosphere coupling.

Atmospheric vertical velocities and advective tendencies are essential large-scale forcing data to drive single-column models (SCMs), cloud-resolving models (CRMs), and large-eddy simulations (LESs). However, they cannot be directly measured from field measurements or easily calculated with great accuracy. In the Atmospheric Radiation Measurement Program (ARM), a constrained variational algorithm (1-D constrained variational analysis (1DCVA)) has been used to derive large-scale forcing data over a sounding network domain with the aid of flux measurements at the surface and top of the atmosphere (TOA). The 1DCVA algorithm is now extended into three dimensions (3DCVA) along with other improvements to calculate gridded large-scale forcing data, diabatic heating sources (Q1), and moisture sinks (Q2). Results are presented for a midlatitude cyclone case study on 3 March 2000 at the ARM Southern Great Plains site. These results are used to evaluate the diabatic heating fields in the available products such as Rapid Update Cycle, ERA-Interim, National Centers for Environmental Prediction Climate Forecast System Reanalysis, Modern-Era Retrospective Analysis for Research and Applications, Japanese 55-year Reanalysis, and North American Regional Reanalysis. We show that although the analysis/reanalysis generally captures the atmospheric state of the cyclone, their biases in the derivative terms (Q1 and Q2) at regional scale of a few hundred kilometers are large and all analyses/reanalyses tend to underestimate the subgrid-scale upward transport of moist static energy in the lower troposphere. The 3DCVA-gridded large-scale forcing data are physically consistent with the spatial distribution of surface and TOA measurements of radiation, precipitation, latent and sensible heat fluxes, and clouds that are better suited to force SCMs, CRMs, and LESs. Possible applications of the 3DCVA are discussed.

Analysis of the cloud components in numerical weather prediction models using advanced data assimilation techniques has been a prime topic in recent years. In this research, the variational data assimilation (DA) system for the Weather Research and Forecasting (WRF) Model (WRFDA) is further developed to assimilate satellite cloud products that will produce the cloud liquid water and ice water analysis. Observation operators for the cloud liquid water path and cloud ice water path are developed and incorporated into the WRFDA system. The updated system is tested by assimilating cloud liquid water path and cloud ice water path observations from Global Geostationary Gridded Cloud Products at NASA. To assess the impact of cloud liquid/ice water path data assimilation on short-term regional numerical weather prediction (NWP), 3-hourly cycling data assimilation and forecast experiments with and without the use of the cloud liquid/ice water paths are conducted. It is shown that assimilating cloud liquid/ice water paths increases the accuracy of temperature, humidity, and wind analyses at model levels between 300 and 150 hPa after 5 cycles (15 h). It is also shown that assimilating cloud liquid/ice water paths significantly reduces forecast errors in temperature and wind at model levels between 300 and 150 hPa. The precipitation forecast skills are improved as well. One reason that leads to the improved analysis and forecast is that the 3-hourly rapid update cycle carries over the impact of cloud information from the previous cycles spun up by the WRF Model.

Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale band-like precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data. Keywords: short-range forecasting, radar, mesoscale convection, reflectivity operator, Z-R relationship (Published: 17 April 2015) Citation: Tellus A 2015, 67, 25047, http://dx.doi.org/10.3402/tellusa.v67.25047

The environments within which high-latitude intense and nonintense mesoscale convective systems (iMCSs and niMCSs) and smaller thunderstorm clusters (sub-MCSs) develop were studied using proximity soundings. MCS statistics covering 8 years were created by analyzing composite radar imagery. One-third of all systems were intense in Finland and the frequency of MCSs was highest in July. On average, MCSs had a duration of 10.8 h and traveled toward the northeast. Many of the linear MCSs had a southwest–northeast line orientation. Interestingly, a few MCSs were observed to travel toward the west, which is a geographically specific feature of the MCS characteristics. The midlevel lapse rate failed to distinguish the environments of the different event types from each other. However, in MCSs, CAPE and the low-level mixing ratio were higher, the deep-layer-mean wind was stronger, and the lifting condensation level (LCL) was lower than in sub-MCSs. CAPE, low-level mixing ratio, and LCL height were the best discriminators between iMCSs and niMCSs. The mean wind over deep layers distinguished the severe wind–producing events from the nonsevere events better than did the vertical equivalent potential temperature difference or the wind shear in shallow layers. No evidence was found to support the hypothesis that dry air at low- and midlevels would increase the likelihood of severe convective winds. Instead, abundant low- and midlevel moisture was present during both iMCS cases and significant wind events. These results emphasize the pronounced role of low- and midlevel moisture on the longevity and intensity of deep moist convection in low-CAPE environments.

The global model analysis and forecast have a significant impact on the regional model predictions, as global model provides the initial and lateral boundary condition to regional model. This study addresses an important question whether the regional model can improve the short-range weather forecast as compared to the global model. The National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) and the Weather Research and Forecasting (WRF) model are used in this study to evaluate the performance of global and regional models over the Indian region. A 24-h temperature and specific humidity forecast from the NCEP GFS model show less error compared to WRF model forecast. Rainfall prediction is improved over the Indian landmass when WRF model is used for rainfall forecast. Moreover, the results showed that high-resolution global model analysis (GFS4) improved the regional model forecast as compared to low-resolution global model analysis (GFS3).

The continuous evolution of global navigation satellite systems (GNSS) meteorology has led to an increased use of associated observations for operational modern low-latency numerical weather prediction (NWP) models, which assimilate GNSS-derived zenith total delay (ZTD) estimates. The development of NWP models with faster assimilation cycles, e.g., 1-h assimilation cycle in the rapid update cycle NWP model, has increased the interest of the meteorological community toward sub-hour ZTD estimates. The suitability of real-time ZTD estimates obtained from three different precise point positioning software packages has been assessed by comparing them with the state-of-the-art IGS final troposphere product as well as collocated radiosonde (RS) observations. The ZTD estimates obtained by BNC2.7 show a mean bias of 0.21 cm, and those obtained by the G-Nut/Tefnut software library show a mean bias of 1.09 cm to the IGS final troposphere product. In comparison with the RS-based ZTD, the BNC2.7 solutions show mean biases between 1 and 2 cm, whereas the G-Nut/Tefnut solutions show mean biases between 2 and 3 cm with the RS-based ZTD, and the ambiguity float and ambiguity fixed solutions obtained by PPP-Wizard have mean biases between 6 and 7 cm with the references. The large biases in the time series from PPP-Wizard are due to the fact that this software has been developed for kinematic applications and hence does not apply receiver antenna eccentricity and phase center offset (PCO) corrections on the observations. Application of the eccentricity and PCO corrections to the a priori coordinates has resulted in a 66 % reduction of bias in the PPP-Wizard solutions. The biases are found to be stable over the whole period of the comparison, which are criteria (rather than the magnitude of the bias) for the suitability of ZTD estimates for use in NWP nowcasting. A millimeter-level impact on the ZTD estimates has also been observed in relation to ambiguity resolution. As a result of a comparison with the established user requirements for NWP nowcasting, it was found that both the G-Nut/Tefnut solutions and one of the BNC2.7 solutions meet the threshold requirements, whereas one of the BNC2.7 solution and both the PPP-Wizard solutions currently exceed this threshold.

For a secure integration of weather dependent renewable energies in Germany's mixed power supply, precise forecasts of expected wind power are indispensable. These in turn are heavily dependent on numerical weather prediction (NWP). With this relevant area of application, NWP models need to be evaluated concerning new variables such as wind speed at hub heights of wind power plants. This article presents verification results of the deterministic NWP forecasts of the global ICON model, its ICON-EU nest, the COSMO-EU, and the COSMO-DE as well as of the ensemble prediction system COSMO-DE-EPS of the German National Weather Service (DWD), against wind mast observations. The focus is on the diurnal cycle in the Planetary Boundary Layer as wind power forecasts for Germany exhibit pronounced systematic amplitude and phase errors in the morning and evening hours. NWP forecasts with lead times up to 48 hours are examined. All considered NWP models reveal shortcomings concerning the representation of the diurnal cycle. Especially in summertime at onshore locations, when Low-Level Jets form, nocturnal wind speeds at hub height are underestimated. In the COSMO model, stable conditions are not sufficiently reflected in the first part of the night and the vertical mixing after sunrise establishes too late. The verification results of the COSMO-DE-EPS confirm the deficiencies of the deterministic forecasts. The deficiencies are present in all ensemble members and thus indicate potential for improvement not only in the model physics parameterization but also concerning the physical ensemble perturbations.

The Nowcasting Satellite Application Facility (NWC SAF) cloud mask from the Meteosat Second Generation (MSG) satellite is introduced in the initialization step of an hourly Rapid Update Cycle (RUC) of the High Resolution Limited Area Model (HIRLAM). MSG cloud-top temperatures and synoptic cloud-base height information are combined at analysis time. This cloud initialization scheme is applied to an experimental run, which is a copy of the operational Royal Netherlands Meteorological Institute [Koninklijk Nederlands Meteorologisch Instituut (KNMI)] RUC model. The experimental run was employed during the period June–December 2011. The RUC model has a forecast length of 6 h. Cloud cover forecasts are verified against MSG cloud cover information and synoptic observations. Forecasts of precipitation, surface pressure, 2-m temperature, and upper-air temperature are verified against synoptic observations and aircraft temperature observations. It is shown that including MSG cloud information in the RUC considerably improves the forecasts of most of these model fields, when compared to the operational control RUC. Both the bias and standard deviation of the errors of the cloud cover forecast are reduced substantially. Forecasts of light precipitation show a slight negative impact, but forecasts of heavy precipitation become better. The bias in 3D temperature fields disappears nearly completely. The error bias of 2-m temperatures has become larger. Two case studies are presented. The first case study had very good forecast performance with respect to low clouds when compared to the reference run. The second case study shows an ambiguous impact; there are still some deficiencies in the cloud initialization and cloud forecast when focusing on a single location.

The impact of assimilating radar radial velocities and reflectivities from the French and German radar network on nowcasting to short-range quantitative precipitation forecasting (SRQPF) was investigated during COPS IOP10. For this purpose, the Weather Research and Forecasting (WRF) model was applied in a convection permitting configuration and a resolution of 3.6 km covering Central Europe. Four different experiments were carried out to study the influence of assimilating 3-dimensional radar data by employing the first developed convection permitting WRF rapid update cycle (RUC) with a 3-h cycle interval over Central Europe. For the control experiment, only conventional observations were selected for assimilation. For the other experiments, radar radial velocities and reflectivities were added. Additionally, the reflectivity operator was slightly modified to study the influence of a smaller lower boundary for the rain water mixing ratio. The results show a positive impact on SRQPF over Eastern France when applying radar radial velocities in addition to conventional observations and Global Positioning System Zenith Total Delay (GPS-ZTD). Radial velocities reduce the overestimation of 3-h precipitation compared to the control experiment without radar data. The bias and RMSE show an improvement of about 10 % respectively. When applying also radar reflectivities in addition, the 3-h precipitation bias is reduced by about 50 % compared to the assimilation of radial velocities only. The frequency bias for different precipitation thresholds now shows values

The 06-km AGL mean wind has been used widely in operations to predict supercell motion. However, when a supercell is low-topped or elevated, its motion may be poorly predicted with this default mean wind— which itself could be height-based or pressure-weighted. This information suggests that a single, fixed layer is inappropriate for some situations, and thus various mean-wind parameters are explored herein. A dataset of 583 observed and 829 Rapid Update Cycle supercell soundings was assembled. When the mean wind is computed using pressure weighting for an effective inflow-layer as the base, and 65% of the most-unstable equilibrium level height as the top, the result is that better supercell motion predictions can be obtained for low-topped and elevated supercells. Such a mean-wind modification would come at the cost of only a minor increase in mean absolute error for the entire sample of supercell cases considered.

The vertical motion and physical structure of elevated convection and generating cells within the comma heads of three continental winter cyclones are investigated using the Wyoming W-band cloud radar mounted on the National Science Foundation/National Center for Atmospheric Research (NSF/NCAR) C-130, supplemented by analyses from the Rapid Update Cycle model and Weather Surveillance Radar-1988 Doppler (WSR-88D) data. The cyclones followed three distinct archetypical tracks and were typical of those producing winter weather in the midwestern United States. In two of the cyclones, dry air in the middle and upper troposphere behind the Pacific cold front intruded over moist Gulf of Mexico air at lower altitudes within the comma head, separating the comma head into two zones. Elevated convection in the southern zone extended from the cold-frontal surface to the tropopause. The stronger convective updrafts ranged from 2 to 7 m s−1 and downdrafts ranged from −2 to −6 m s−1. The horizontal scale of the convective cells was approximately 5 km. The poleward zone of the comma head was characterized by deep stratiform clouds topped by cloud-top generating cells that reached the tropopause. Updrafts and downdrafts within the generating cells ranged from 1 to 2 m s−1, with the horizontal scale of the cells from about 1 to 2 km. Precipitation on the poleward side of the comma head conformed to a seeder–feeder process—the generating cells seeding the stratiform cloud—which was forced by synoptic-scale ascent. In one case, shallow clouds behind the cyclone’s cold front were also topped by cloud-top generating cells, with vertical motions ranging from 1 to 2 m s−1.

A three-dimensional idealized cloud model was used to study the storm-scale differences between simulated supercells that produce tornado-like vortices and those that do not. Each simulation was initialized with a different Rapid Update Cycle, version 2 (RUC-2), sounding that was associated with tornadic and nontornadic supercells in nature. The focus is an analysis of vorticity along backward-integrated trajectories leading up to tornadogenesis (19 simulations) and tornadogenesis failure (14 simulations). In so doing, the differences between the nontornadic and tornadic cases can be explored in relation to their associated environmental sounding. Backward-integrated trajectories seeded in the near-surface circulation indicate that the largest differences in vertical vorticity production between the tornadic and nontornadic simulations occur in parcels that descend to the surface from aloft (i.e., descending). Thus, the results from this study support the hypothesis that descending air in the rear of the storm is crucial to tornadogenesis. In the tornadic simulations, the descending parcels experience more negative vertical vorticity production during descent and larger tilting of horizontal vorticity into positive vertical vorticity after reaching the surface, owing to stronger horizontal gradients of vertical velocity. The larger vertical velocities experienced by the trajectories just prior to tornadogenesis in the tornadic simulations are associated with environmental soundings of larger CAPE, smaller convective inhibition (CIN), and larger 0–1-km storm-relative environmental helicity. Furthermore, in contrast with what might be expected from previous works, trajectories entering the incipient tornadic circulations are more negatively buoyant than those entering the nontornadic circulations.

The Weather Research and Forecasting (WRF) model’s ability to forecast convective morphological evolution is examined for 37 convective systems. The simulations used Thompson microphysics with 3-km horizontal grid spacing. Ten convective mode classifications were used. An objective score was developed to determine the accuracy of the simulated morphologies considering a normalized duration of each mode simulated and its agreement with observations. Rapid Update Cycle analyses were used to compare larger-scale preinitiation conditions to simulated morphology accuracy, as well as to examine how the WRF model’s skill at predicting these larger-scale conditions influenced its prediction of morphology. Two case studies selected as representative of the most common simulated morphology deficiencies were examined in detail. The model simulated cellular systems relatively well but struggled more with linear systems, particularly bow echoes and squall lines having trailing stratiform rain regions. Morphological evolution was generally better simulated in environments with enhanced deep-layer shear and cooler potential temperatures at the level of maximum θe. Weaker deep-layer shear, cooler potential temperatures at the surface, and quickly warming potential temperatures with height increased the likelihood of timing errors. The first case study showed that a warmer cold pool, much larger line-normal shear, and excessive midlevel drying were present in the model run that failed to develop a trailing stratiform region. The second case study showed that weak shear and the absence of a well-developed cold pool may have played a role in the lack of bowing.