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Elastic lidar (532 nm) measurements were carried out by the Atmospheric Lidar Group at the University of Maryland, Baltimore County (UMBC, 39.25° N, 76.70° W) during meteorological conditions that favored the formation of the nocturnal low level jet (NLLJ) over the United States Mid-Atlantic region. The lidar timeseries from three case studies showed the intrusion of the NLLJ air mass into the nocturnal boundary layer (NBL) and the subsequent displacement of aerosols that were used for tracing atmospheric motion. Three distinctive regions were identified in the timeseries: 1) a wedging zone within the residual layer, that was heavily laden with aerosols, at the onset of the NLLJ, 2) a lofted layer of particulates above the “clean” jet core, and 3) a region where the lofted layer collapsed that was conterminous with the diminishing NLLJ below, and characterized by downward mixing analogous to turbulent wake regions. The National Oceanic and Atmospheric Administration’s (NOAA) Rapid Update Cycle (RUC, grid 252) model was used to analyze the horizontal extent and vertical structure of the NLLJ and to compare with observations acquired during these events. A conceptual model is proposed to highlight the role of the NLLJ, during similar weather patterns, in the regional transport of pollutants and their impact on poor air quality episodes in the Mid-Atlantic United States.

Supercell thunderstorms produce a disproportionate amount of the severe weather in the United States, and accurate prediction of their movement and evolution is needed to warn the public of their hazards. This study explores the practical predictability of supercell thunderstorm forecasts in the presence of typical errors in the preconvective environmental conditions. The Advanced Research Weather Research and Forecasting model (ARW-WRF) is run at 1-km grid spacing and a control run of a supercell thunderstorm is produced using a horizontally homogeneous environment. Forecast errors from supercell environments derived from the 13-km Rapid Update Cycle (RUC) valid at 0000 UTC for forecast lead times up to 3 h are used to define the environmental errors, and 100 runs initialized with environmental perturbations characteristic of those errors are produced for each lead time. The simulations are analyzed to determine the spread and practical predictability of supercell thunderstorm forecasts from a storm-scale model, with the control used as truth. Most of the runs perturbed with the environmental forecast errors produce supercell thunderstorms; however, there is much less predictability for storm motion and structure. Results suggest that an upper bound to the practical predictability of storm location with the current environmental uncertainty for a 1-h environmental forecast is about 2 h, with the predictability of the storms decreasing to 1 h as lead time increases. Smaller-scale storm features, such as midlevel mesocyclones and regions of heavy rainfall, display much less predictability than storm location. Mesocyclone location is predictable out to 40 min or less, while heavy 5-min rainfall location is not predictable.

This study evaluates 24-h forecasts of dryline position from an experimental 4-km grid-spacing version of the Weather Research and Forecasting Model (WRF) run daily at the National Severe Storms Laboratory (NSSL), as well as the 12-km grid-spacing North America Mesoscale Model (NAM) run operationally by the Environmental Modeling Center of NCEP. For both models, 0000 UTC initializations are examined, and for verification 0000 UTC Rapid Update Cycle (RUC) analyses are used. For the period 1 April–30 June 2007–11, 116 cases containing drylines in all three datasets were identified using a manual procedure that considered specific humidity gradient magnitude, temperature, and 10-m wind. For the 24-h NAM forecasts, no systematic east–west dryline placement errors were found, and the majority of the east–west errors fell within the range ±0.5° longitude. The lack of a systematic bias was generally present across all subgroups of cases categorized according to month, weather pattern, and year. In contrast, a systematic eastward bias was found in 24-h NSSL-WRF forecasts, which was consistent across all subgroups of cases. The eastward biases seemed to be largest for the subgroups that favored “active” drylines (i.e., those associated with a progressive synoptic-scale weather system) as opposed to “quiescent” drylines that tend to be present with weaker tropospheric flow and have eastward movement dominated by vertical mixing processes in the boundary layer.

An indirect radar reflectivity assimilation scheme has been developed within the Weather Research and Forecasting model three-dimensional data assimilation system (WRF 3D-Var). This scheme, instead of assimilating radar reflectivity directly, assimilates retrieved rainwater and estimated in-cloud water vapor. An analysis is provided to show that the assimilation of the retrieved rainwater avoids the linearization error of the Z–qr (reflectivity–rainwater) equation. A new observation operator is introduced to assimilate the estimated in-cloud water vapor. The performance of the scheme is demonstrated by assimilating reflectivity observations into the Rapid Update Cycle data assimilation and forecast system operating at Beijing Meteorology Bureau. Four heavy-rain-producing convective cases that occurred during summer 2009 in Beijing, China, are studied using the newly developed system. Results show that on average the assimilation of reflectivity significantly improves the short-term precipitation forecast skill up to 7 h. A diagnosis of the analysis fields of one case shows that the assimilation of reflectivity increases humidity, rainwater, and convective available potential energy in the convective region. As a result, the analysis successfully promotes the developments of the convective system and thus improves the subsequent prediction of the location and intensity of precipitation for this case.

Five western disturbances (WDs) are simulated using the National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model with different land surface parameterization (LSP) schemes. The LSP schemes used in this study are thermal diffusion, Noah and rapid update cycle (RUC). The spatial distribution and area averaged values of 24 h accumulated rainfall simulated by the model using above LSP schemes are compared with corresponding Tropical Rainfall Measuring Mission (TRMM) observed values. The model simulated rainfall is also compared with those obtained from India Meteorological Department (IMD) station observations. Area averaged root mean square error (RMSE) in rainfall obtained from the three LSP schemes for the five cases are also compared against those in TRMM. Results show that the rainfall obtained using the RUC LSP scheme is closer to the observed values in comparison to those obtained in the thermal diffusion and Noah LSP schemes. It is clear from the results that circulation features and precipitation amounts obtained by the model are sensitive to LSP schemes. Compared to the verification analysis, the wind patterns at 850, 500 and 200 hPa are simulated by the model with reasonable accuracy. The relative humidity and mean sea level pressure are also well simulated by the model when RUC LSP is used. Copyright © 2013 Royal Meteorological Society

This paper uses an urbanized high-resolution land data assimilation system (u-HRLDAS) to parameterize the urban land surface characteristics. The u-HRLDAS model is localized and developed in order to satisfy the need of the weather forecast in Beijing, China. The remote sensing data used to localize and drive u-HRLDAS include the soil type data and MODIS retrieved leaf area index (LAI) data. The evaporation and water depth for impervious surface in urban area are developed to improve the simulation of u-HRLDAS. The result of the urban weather forecast is used for the comparison based on the rapid update cycle system at Beijing Meteorological Bureau (BJ-RUC) without coupled with u-HRLDAS. The land surface temperature, land surface fluxes, and first layer soil moisture in several single sites and urban Beijing region by BJ-RUC are compared with u-HRLDAS after localization and development. The off-line simulation results indicate that compared with BJ-RUC, after the localization and development, u-HRLDAS can improve the simulation of land surface parameters and fluxes definitely.

A gridded, hourly, three-dimensional environmental mesoanalysis database at the Storm Prediction Center (SPC), based on objectively analyzed surface observations blended with the Rapid Update Cycle (RUC) model-analysis fields and described in Parts I and II of this series, is applied to a 2003–11 subset of the SPC tropical cyclone (TC) tornado records. Distributions of environmental convective parameters, derived from SPC hourly mesoanalysis fields that have been related to supercells and tornadoes in the midlatitudes, are evaluated for their pertinence to TC tornado occurrence. The main factor differentiating TC from non-TC tornado environments is much greater deep-tropospheric moisture, associated with reduced lapse rates, lower CAPE, and smaller and more compressed distributions of parameters derived from CAPE and vertical shear. For weak and strong TC tornado categories (EF0–EF1 and EF2–EF3 on the enhanced Fujita scale, respectively), little distinction is evident across most parameters. Radar reflectivity and velocity data also are examined for the same subset of TC tornadoes, in order to determine parent convective modes (e.g., discrete, linear, clustered, supercellular vs nonsupercellular), and the association of those modes with several mesoanalysis parameters. Supercellular TC tornadoes are accompanied by somewhat greater vertical shear than those occurring from other modes. Tornadoes accompanying nonsupercellular radar echoes tend to occur closer to the TC center, where CAPE and shear tend to weaken relative to the outer TC envelope, though there is considerable overlap of their respective radial distributions and environmental parameter spaces.

An accurate short-term weather forecast enhances the performance of model-based control strategies for a building. Most existing short-term forecast methods and models are based on historical archives of weather variables observed about an individual building. This practice leads to ask: 1) Does a short-term forecast model based on a collection of the past data have sufficient potential to predict future behavior, which is inherently random?; 2) If historical data is not available, or if only a limited number of weather variables is available, what are the other alternatives?; and 3) Would it be possible to derive a relevant weather data set from an incomplete historical data-driven model? The National Digital Forecast Database (NDFD) XML may answer these questions. This paper discusses an applicability of the NDFD XML for model-based building control solutions. It also presents a simplified, easily implementable, and reliable method to predict hourly global horizontal solar radiation. The short-term weather forecast method using the NDFD XML shows an outstanding performance of forecasting erratic and sporadic characteristics of weather, particularly when compared to the historical data-driven method. However, its forecast performance is not always accurate due to the inherent irregularity of weather. In order to increase robustness of model-based controls, we recommend that short-term weather forecasts should be considered as possessing scenario uncertainty . Including multiple scenarios into formulation of control problem can effectively describe scenario uncertainty when part of scenarios account for such erratic nature.

This paper reports an application of Numerical Weather Prediction (NWP) in the heat load forecasting field. The NWP is applied to obtain the correlated weather parameters of the heat load, and then the properly structured Artificial Neural Network (ANN) model is designed to perform the prediction. Satisfactory experimental results are obtained by using actual heat load data in the north of China. The experimental result shows that the proposed NWP based method can predict the heat load precisely. Comparing with the traditional weather forecast based-method, the proposed method can effectively improve the forecasting accuracy.

International audience; Temperature and humidity retrievals from an international network of ground-based microwave radiometers (MWRs) have been collected to assess the potential of their assimilation into a convective-scale numerical weather prediction (NWP) system. Thirteen stations over a domain encompassing the western Mediterranean basin were considered for a time period of 41 days in autumn, when heavy precipitation events most often plague this area.Prior to their assimilation, MWR data were compared to very-short-term forecasts. Observation-minus-background statistics revealed some biases, but standard deviations were comparable to that obtained with radiosondes. The MWR data were then assimilated in a three-dimensional variational data assimilation system through the use of a rapid update cycle. A first set of four different experiments were designed to assess the impact of the assimilation of temperature and humidity profiles, both separately and jointly. This assessment was done through the use of a comprehensive dataset of upper-air and surface observations collected in the framework of the HyMeX programme.The results showed that the impact was generally very limited on all verified parameters, except for precipitation. The impact was found to be generally beneficial in terms of most verification metrics for about 18 h, especially for larger accumulations. Two additional data-denial experiments showed that even more positive impact could be obtained when MWR data were assimilated without other redundant observations. The conclusion of the study points to possible ways of enhancing the impact of the assimilation of MWR data in convective-scale NWP systems.

Extending storm classification into a separation of convective storm types has applications to nowcasting, severe weather warning and quantitative precipitation estimation and forecasting. This work presents an exploration of an automated classification scheme that uses a combination of radar reflectivity products and near-storm environmental parameters derived from Rapid Update Cycle (RUC) model analyses to assign one of eight classes to each storm observed. An assessment is made of the classification accuracy, and it is found that the scheme that uses the combination of radar and model data outperforms the scheme that uses only radar products. This is particularly notable in the identification of storms that have rotation, and therefore improves the assessment of those storms' potential longevity and severe weather threat.

This study uses radiosonde observations obtained during the second phase of the Verification of the Origins of Rotation in Tornadoes Experiment (VORTEX2) to verify base-state variables and severe-weather-related parameters calculated from Rapid Update Cycle (RUC) analyses and 1-h forecasts, as well as those calculated from the operational surface objective analysis system used at the Storm Prediction Center (the SFCOA). The rapid growth in temperature, humidity, and wind errors from 0 to 1 h seen at all levels in a past RUC verification study by Benjamin et al. is not seen in the present study. This could be because the verification observations are also assimilated into the RUC in the Benjamin et al. study, whereas the verification observations in the present study are not. In the upper troposphere, the present study shows large errors in relative humidity, mostly related to a large moist bias. The planetary boundary layer tends to be too shallow in the RUC analyses and 1-h forecasts. Wind speeds tend to be too fast in the lowest 1 km and too slow in the 2–4-km layer. RUC and SFCOA 1-h forecast errors for many important severe weather parameters are large relative to their potential impact on convective evolution. However, the SFCOA significantly improves upon the biases seen in most of the 1-h RUC forecasts for the base-state surface variables and most of the other severe-weather-related parameters, indicating that the SFCOA has a more significant impact in reducing the biases in the 1-h RUC forecasts than on the root-mean-squared errors.

A new hydrometeor classification algorithm that combines thermodynamic output from the Rapid Update Cycle (RUC) model with polarimetric radar observations is introduced. The algorithm improves upon existing classification techniques that rely solely on polarimetric radar observations by using thermodynamic information to help to diagnose microphysical processes (such as melting or refreezing) that might occur aloft. This added information is especially important for transitional weather events for which past studies have shown radar-only techniques to be deficient. The algorithm first uses vertical profiles of wet-bulb temperature derived from the RUC model output to provide a background precipitation classification type. According to a set of empirical rules, polarimetric radar data are then used to refine precipitation-type categories when the observations are found to be inconsistent with the background classification. Using data from the polarimetric KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) located in Norman, Oklahoma, the algorithm is tested on a transitional winter-storm event that produced a combination of rain, freezing rain, ice pellets, and snow as it passed over central Oklahoma on 30 November 2006. Examples are presented in which the presence of a radar bright band (suggesting an elevated warm layer) is observed immediately above a background classification of dry snow (suggesting the absence of an elevated warm layer in the model output). Overall, the results demonstrate the potential benefits of combining polarimetric radar data with thermodynamic information from numerical models, with model output providing widespread coverage and polarimetric radar data providing an observation-based modification of the derived precipitation type at closer ranges.

The increase in demand for air travel throughput has resulted in an effort to identify novel techniques for improving efficiency. One such effort targets the barrier imposed by wake turbulence separation standards—reducing interaircraft spacing during radar operations. Recently, concepts have focused on exploiting the effects of wake transport under wind fields by developing wind-dependent procedures that mitigate wake hazards during favorable wind conditions. However, the challenge with regard to computing benefits during conditional time epochs requires methodology designed for a niche modeling area that even sophisticated airport capacity simulation models lack. This work attempts to augment the capacity computation by presenting a characterization of the favorable crosswind availability across a range of thresholds and aloft monitoring heights based on Rapid Update Cycle and aviation system performance measure weather data. The focus is on presenting a generalization of wind behavior and on providing insight into the effects of the wind on the applicability of wind-dependent wake-based procedures. Data for nine major U.S. airports currently operating closely spaced parallel runways are analyzed.

Recent literature has identified several supercell/tornado forecast parameters in common use that are operationally beneficial in assessing environments supportive of supercell tornadoes. These parameters are utilized in the computation of tornado forecast guidance such as the significant tornado parameter (STP), a dimensionless parameter developed at the Storm Prediction Center (SPC) that applies a subjectively chosen scale. The goal of this research is to determine if useful logistic regression equations can be developed to estimate the conditional probability of supercell tornadoes that are categorized as level 2 or stronger on the enhanced Fujita scale (EF) when a similar set of environmental background parameters is applied as variables. A large database of Rapid Update Cycle (RUC) analysis soundings in proximity to a representative sample of tornadic and nontornadic supercells over the central and eastern United States, a number of which were associated with EF2 or stronger tornadoes, was used to compute supercell tornado forecast parameters similar to those in the original version of STP. Three logistic regression equations were developed from this database, two of which are described and analyzed in detail. Statistical verification for both equations was accomplished using independent data from 2008 in proximity to supercell storms identified by staff at SPC. A recent version of the STP was utilized as a comparison diagnostic to accomplish part of the statistical verification. The results of this research suggest that output from both logistic regression equations can provide valuable guidance in a probabilistic sense, when adjustments are made for the ongoing convective mode. Case studies presented also suggest that this guidance can provide information complementary to STP in severe weather situations with potential for supercell tornadoes.

The meteorological surveillance of the four nuclear power plants in Switzerland is of first importance in a densely populated area such as the Swiss Plateau. The project "Centrales Nucléaires et Météorologie" CN-MET aimed at providing a new security tool based on one hand on the development of a high resolution numerical weather prediction (NWP) model. The latter is providing essential nowcasting information in case of a radioactive release from a nuclear power plant in Switzerland. On the other hand, the model input over the Swiss Plateau is generated by a dedicated network of surface and upper air observations including remote sensing instruments (wind profilers and temperature/humidity passive microwave radiometers). This network is built upon three main sites ideally located for measuring the inflow/outflow and central conditions of the main wind field in the planetary boundary layer over the Swiss Plateau, as well as a number of surface automatic weather stations (AWS). The network data are assimilated in real-time into the fine grid NWP model using a rapid update cycle of eight runs per day (one forecast every three hours). This high resolution NWP model has replaced the former security tool based on in situ observations (in particular one meteorological mast at each of the power plants) and a local dispersion model. It is used to forecast the dynamics of the atmosphere in the planetary boundary layer (typically the first 4 km above ground layer) and over a time scale of 24 h. This tool provides at any time (e.g. starting at the initial time of a nuclear power plant release) the best picture of the 24-h evolution of the air mass over the Swiss Plateau and furthermore generates the input data (in the form of simulated values substituting in situ observations) required for the local dispersion model used at each of the nuclear power plants locations. This paper is presenting the concept and two validation studies as well as the results of an emergency response exercise performed in winter 2009.

Previous research suggests that an ensemble Kalman filter (EnKF) data assimilation and modeling system can produce accurate atmospheric analyses and forecasts at 30–50-km grid spacing. This study examines the ability of a mesoscale EnKF system using multiscale (36/12 km) Weather Research and Forecasting (WRF) model simulations to produce high-resolution, accurate, regional surface analyses, and 6-h forecasts. This study takes place over the complex terrain of the Pacific Northwest, where the small-scale features of the near-surface flow field make the region particularly attractive for testing an EnKF and its flow-dependent background error covariances. A variety of EnKF experiments are performed over a 5-week period to test the impact of decreasing the grid spacing from 36 to 12 km and to evaluate new approaches for dealing with representativeness error, lack of surface background variance, and low-level bias. All verification in this study is performed with independent, unassimilated observations. Significant surface analysis and 6-h forecast improvements are found when EnKF grid spacing is reduced from 36 to 12 km. Forecast improvements appear to be a consequence of increased resolution during model integration, whereas analysis improvements also benefit from high-resolution ensemble covariances during data assimilation. On the 12-km domain, additional analysis improvements are found by reducing observation error variance in order to address representativeness error. Removing model surface biases prior to assimilation significantly enhances the analysis. Inflating surface wind and temperature background error variance has large impacts on analyses, but only produces small improvements in analysis RMS errors. Both surface and upper-air 6-h forecasts are nearly unchanged in the 12-km experiments. Last, 12-km WRF EnKF surface analyses and 6-h forecasts are shown to generally outperform those of the Global Forecast System (GFS), North American Model (NAM), and the Rapid Update Cycle (RUC) by about 10%–30%, although these improvements do not extend above the surface. Based on these results, future improvements in multiscale EnKF are suggested.

Forecasting the weather diseases is a complex process with strong uncertainty. In order to improve the forecast accuracy, the paper proposed a sort of forecast model for weather diseases based on the artificial neural network. By means of ANN, lots of sample data could be trained so as to optimize the connecting weighting value of ANN, finally the forecast effect of weather diseases has been intuitively verified by experiment simulation. The factor resulted in weather diseases could be the following weather parameters, such as temperature of a day, relative humidity, air pressure, wind speed and so on. The paper takes respiratory disease treatment records as an example, the simulation result points out that it is consistent between the change trend of being sick and the actual situation. The study result shows that the model is reasonable and feasible, and could forecast weather disease more effectively.

The principle of the energy equilibrium is applied to develop the relationship between heat loss and supply, indoor and outdoor temperature, supply and return water temperature of heating system for a building. A numerical simulation model of supply-return water temperature is deduced. Based on the hourly air temperatures for next 24 hours of Rapid Update Cycle (BJ-RUC) NWP and observed indoor temperature from Haiyuan Community in February 2008, a simulation experiment of hourly prediction for next 24-hours was conducted. The experiments show that the mean square root error between simulated indoor temperature and required temperature (20°C) is 0.26°C comparing with the actual error of operation in Beijing Haiyuan Community 1.69°C. This means the prediction model of supply-return water temperature has a good quality. Comparing with the statistical or empirical method in the past, it is the first time in China to utilize dynamic method to forecast heating parameters which could makes refined heating weather service possible.

Composite environments of mesoscale convective systems (MCSs) are produced from Rapid Update Cycle (RUC) analyses to explore the differences between rapidly and slowly developing MCSs as well as the differences ahead of long- and short-lived MCSs. The composite analyses capture the synoptic-scale features known to be associated with MCSs and depict the inertial oscillation of the nocturnal low-level jet (LLJ), which remains strong but tends to veer away from decaying MCSs. The composite first storms environment for the rapidly developing MCSs contains a stronger LLJ located closer to the first storms region, much more conditional instability, potential instability, and energy available for downdrafts, smaller 3–10-km vertical wind shear, and smaller geostrophic potential vorticity in the upper troposphere, when compared to the environment for the slowly developing MCSs. The weaker shear above 3 km for the rapidly developing MCSs is consistent with supercell or discrete cell modes being less likely in weaker deep-layer shear and the greater potential for a cold pool to trigger convection when the shear is confined to lower levels. Furthermore, these results suggest that low values of upper-level potential vorticity may signal a rapid transition to an MCS. The composite environment ahead of the genesis of long-lived MCSs contains a broader LLJ, a better-defined frontal zone, stronger low-level frontogenesis, deeper moisture, and stronger wind shear above 2 km, when compared to short-lived MCSs. The larger shear above 2 km for the long-lived MCSs is consistent with the importance of shear elevated above the ground to help organize and maintain convection that feeds on the elevated unstable parcels after dark and is indicative of the enhanced baroclinicity ahead of the MCSs.

A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reflectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments.

An analysis of a regional severe weather outbreak that was related to a mesoscale convective vortex (MCV) is performed. The MCV-spawning mesoscale convection system (MCS) formed in northwest Kansas along the southern periphery of a large cutoff 500-hPa low centered over western South Dakota. As the MCS propagated into eastern Kansas during the early morning of 1 June 2007, an MCV that became evident from multiple data sources [e.g., Weather Surveillance Radar-1988 Doppler (WSR-88D) network, visible satellite imagery, wind-profiler data, Rapid Update Cycle 1-hourly analyses] tracked through northwest Missouri and central Iowa and manifested itself as a well-defined midlevel short-wave trough. Downstream of the MCV in southeast Iowa and northwest Illinois, southwesterly 500-hPa winds increased to around 25 m s−1 over an area with southeasterly surface winds and 500–1500 J kg−1 of surface-based convective available potential energy (CAPE), creating a favorable environment for severe weather. In the favorable region, multiple tornadoes occurred, including one rated as a category 3 storm on the enhanced Fujita scale (EF3) that caused considerable damage. In the analysis, emphasis is placed on the role of the MCV in leading to a favorable environment for severe weather. In addition, convection-allowing forecasts of the MCV and associated environmental conditions from the 10-member Storm-Scale Ensemble Forecast (SSEF) system produced for the 2007 NOAA Hazardous Weather Testbed Spring Experiment are compared to those from a similarly configured, but coarser, 30-member convection-parameterizing ensemble. It was found that forecasts of the MCV track and associated environmental conditions (e.g., midlevel winds, low-level wind shear, and instability) were much better in the convection-allowing ensemble. Errors in the MCV track from convection-parameterizing members likely resulted from westward displacement errors in the incipient MCS. Furthermore, poor depiction of MCV structure and maintenance in convection-parameterizing members, which was diagnosed through a vorticity budget analysis, likely led to the relatively poor forecasts of the associated environmental conditions. The results appear to be very encouraging for convection-allowing ensembles, especially when environmental conditions lead to a high degree of predictability for MCSs, which appeared to be the case for this particular event.

A two-dimensional variational method is used to analyze 2-m air temperatures over a limited domain (4° latitude × 4° longitude) in order to evaluate approaches to examining the sensitivity of the temperature analysis to the specification of observation and background errors. This local surface analysis (LSA) utilizes the 1-h forecast from the Rapid Update Cycle (RUC) downscaled to a 5-km resolution terrain level for its background fields and observations obtained from the Meteorological Assimilation Data Ingest System. The observation error variance as a function of broad network categories and the error variance and covariance of the downscaled 1-h RUC background fields are estimated using a sample of over 7 million 2-m air temperature observations in the continental United States collected during the period 8 May–7 June 2008. The ratio of observation to background error variance is found to be between 2 and 3. This ratio is likely even higher in mountainous regions where representativeness errors attributed to the observations are large. The technique used to evaluate the sensitivity of the 2-m air temperature to the ratio of the observation and background error variance and background error length scales is illustrated over the Shenandoah Valley of Virginia for a particularly challenging case (0900 UTC 22 October 2007) when large horizontal temperature gradients were present in the mountainous regions as well as over two entire days (20 and 27 May 2009). Sets of data denial experiments in which observations are randomly and uniquely removed from each analysis are generated and evaluated. This method demonstrates the effects of overfitting the analysis to the observations.

Climatological characteristics of mesoscale convective vortices (MCVs) occurring in the state of Oklahoma during the late spring and summer of four years are investigated. The MCV cases are selected based on vortex detection by an objective algorithm operating on analyses from the Rapid Update Cycle (RUC) model. Consistent with a previous study, true MCVs represent only about 20% of the mesoscale relative vorticity maxima detected by the algorithm. The MCVs have a broad range of radii and intensities, and their longevities range between 1 and 54 h. Their median radius is about 200 km, and their median midlevel relative vorticity is 1.2 × 10−4 s−1. There appears to be no significant relationship between MCV longevity and intensity. Similar to past estimates, approximately 40% of the MCVs generate secondary convection within their circulations. The mean synoptic-scale MCV environment is determined by the use of a RUC-based composite analysis at four different stages in the MCV life cycle, defined based on vortex detection by the objective algorithm. MCV initiation is closely tied to the diurnal cycle of convection over the Great Plains, with MCVs typically forming in the early morning, near the time of maximum extent of nocturnal mesoscale convective systems (MCSs). Features related to the parent MCSs, including upper-level divergent outflow, midlevel convergence, and a low-level jet, are prominent in the initiating MCV composite. The most significant feature later in the MCV life cycle is a persistent mesoscale trough in the midlevel height field. The potential vorticity (PV) structure of the composite MCV consists of a midlevel maximum and an upper-level minimum, with some extension of elevated PV into the lower troposphere as the vortex matures. The environment immediately downshear of the MCV is more conducive to secondary convection than the environment upshear of the MCV. This midlatitude MCV climatology represents an extension of past individual case studies by providing mean characteristics of a large MCV population; these statistics are suitable for the verification of MCV simulations. Also presented is the first high-resolution composite analysis of the MCV environment at different stages of the MCV life cycle, which will aid in identifying and forecasting these systems.

A multiyear evaluation of a regional aircraft observation system [Tropospheric Aircraft Meteorological Data Reports (TAMDAR)] is presented. TAMDAR observation errors are compared with errors in traditional reports from commercial aircraft [aircraft meteorological data reports (AMDAR)], and the impacts of TAMDAR observations on forecasts from the Rapid Update Cycle (RUC) over a 3-yr period are evaluated. Because of the high vertical resolution of TAMDAR observations near the surface, a novel verification system has been developed and employed that compares RUC forecasts against raobs every 10 hPa; this revealed TAMDAR-related positive impacts on RUC forecasts—particularly for relative humidity forecasts—that were not evident when only raob mandatory levels were considered. In addition, multiple retrospective experiments were performed over two 10-day periods, one in winter and one in summer; these allowed for the assessment of the impacts of various data assimilation strategies and varying data resolutions. TAMDAR’s impacts on 3-h RUC forecasts of temperature, relative humidity, and wind are found to be positive and, for temperature and relative humidity, substantial in the region, altitude, and time range over which TAMDAR-equipped aircraft operated during the studied period of analysis.

An assessment is presented on the relative forecast impact on the performance of a numerical weather prediction model from eight different observation data types: aircraft, profiler, radiosonde, velocity azimuth display (VAD), GPS-derived precipitable water, aviation routine weather report (METAR; surface), surface mesonet, and satellite-based atmospheric motion vectors. A series of observation sensitivity experiments was conducted using the Rapid Update Cycle (RUC) model/assimilation system in which various data sources were denied to assess the relative importance of the different data types for short-range (3–12 h) wind, temperature, and relative humidity forecasts at different vertical levels and near the surface. These experiments were conducted for two 10-day periods, one in November–December 2006 and one in August 2007. These experiments show positive short-range forecast impacts from most of the contributors to the heterogeneous observing system over the RUC domain. In particular, aircraft observations had the largest overall impact for forecasts initialized 3–6 h before 0000 or 1200 UTC, considered over the full depth (1000–100 hPa), followed by radiosonde observations, even though the latter are available only every 12 h. Profiler data (including at a hypothetical 8-km depth), GPS-precipitable water estimates, and surface observations also led to significant improvements in short-range forecast skill.

The Weather Research and Forecasting (WRF) model is run in various configurations for a five day cold weather period with multi-day inversions over Interior Alaska. Comparison of the simulations with radiosonde data and surface observations shows that WRF's performance for these inversions strongly depends on the physical packages chosen. Simulated near-surface air temperatures as well as dew-point temperatures differ about 4 K on average depending on the physical packages used. All simulations have difficulties in capturing the full strength of the surface temperature inversion and in simulating strong variations of dew-point temperature profiles. The greatest discrepancies between simulated and observed vertical profiles of temperature and dew-point temperature occur around the levels of great wind shear. Out of the configurations tested the radiation schemes of the Community Atmosphere Model combined with the Rapid Update Cycle land surface model and modified versions of the Medium Range Forecast model's surface layer and atmospheric boundary layer schemes capture the inversion situation best most of the time.

We establish an on-line optimization framework to exploit weather forecast information in the operation of energy systems. We argue that anticipating the weather conditions can lead to more proactive and cost-effective operations. The framework is based on the solution of a stochastic dynamic real-time optimization (D-RTO) problem incorporating forecasts generated from a state-of-the-art weather prediction model. The necessary uncertainty information is extracted from the weather model using an ensemble approach. The accuracy of the forecast trends and uncertainty bounds are validated using real meteorological data. We present a numerical simulation study in a building system to demonstrate the developments.

In recent years the 'Nowcasting Wake Vortex Impact Variables' model NOWVIV has been developed at the Deutsches Zentrum für Luft- und Raumfahrt, DLR, to forecast weather parameters in airport environments. The German Meteorological Service, DWD, employs his COSMO-DE model (COnsortium for Small scale Modelling-DEutschland) for operational forecasts in Germany. A systematic comparison of model output from NOWVIV and a derivate of COSMO-DE, named COSMO-FRA, is presented. Both models are centred at Frankfurt Airport with horizontal resolutions of 2.1 km and 2.8 km, respectively. In the DLR Project Wetter & Fliegen the COSMO-FRA model will replace the NOWVIV model and become a key component in the future rapid update cycle for adverse weather predictions at the airports of Frankfurt and Munich. The forecast vertical profiles of runway crosswind, head/tail wind, temperature, and turbulent kinetic energy (TKE) are validated against Wind and Temperature Radar (WTR/RASS) measurements operated by the German Air Navigation Service Provider, DFS, within a 40 day period during fall 2004 and a 60 day period during winter 2007. Model and WTR output is provided every 10 minutes. In general it was found that the predictions of both models yield similar skills based on the root mean square error (RMSE) and mean bias statistics of the crosswind as well as the false alarm-rate (FAR) statistics in forecasting different crosswind thresholds. The RMSE of crosswind between ground and 1600 m altitude ranges between 2.2 and 3.0 ms-1 for NOWVIV in fall and winter. For COSMO-FRA this error ranges between 2.0 and 2.5 ms-1 during winter and 2.5 and 3.5 ms-1 during fall, respectively. The FAR for exceeding a crosswind threshold of 3 ms-1 is about 23 % in fall and 17 % in winter for both models.

A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.

Edwards and Thompson have made comprehensive and thorough comments concerning the methods, ac¬curacy, and results in a case study by Kellenbenz et al. These comments questioned the representativeness of model-derived soundings and graphics used, as well as the modification methods employed with soundings presented in the 18 July 2004 case study. Other issues included the application of previous database studies to lifting condensation level (LCL) values found in the 18 July 2004 examination, as well as a focus that em¬phasized a single tornadic storm. In this response, the authors address problems and oversights with the 18 July 2004 case study, using new data to conduct a reanalysis of the environment on that evening. Additionally, a large independent database of Rapid Update Cycle (RUC) analysis soundings associated with supercell tornadoes is used to provide the context from which to carefully evaluate LCL results from the 18 July 2004 case relative to significant and violent tornadoes. Results indicate that although the LCL height associated with the F4 tornadic supercell in the original case study was probably overestimated, the background LCL environment was still unusually high for a violent tornado. New material presented in this response reinforces the conclusion that, when LCL heights are at the far upper end of empirical study distributions associated with significant tornadoes, undue weight should not be given to LCL as a tornado probability reduction factor when CAPE-storm relative helicity (SRH) combinations and deep-layer shear are also strong.

Widespread moderate turbulence was recorded on three specially equipped commercial airline flights over northern Kansas near the northern edge of the extensive cirrus anvil of a nocturnal mesoscale convective system (MCS) on 17 June 2005. A noteworthy aspect of the turbulence was its location several hundred kilometers from the active deep convection (i.e., large reflectivity) regions of the MCS. Herein, the MCS life cycle and the turbulence environment in its upper-level outflow are studied using Rapid Update Cycle (RUC) analyses and cloud-permitting simulations with the Weather Research and Forecast Model (WRF). It is demonstrated that strong vertical shear beneath the MCS outflow jet is critical to providing an environment that could support dynamic (e.g., shearing type) instabilities conducive to turbulence. Comparison of a control simulation to one in which the temperature tendency due to latent heating was eliminated indicates that strong vertical shear and corresponding reductions in the local Richardson number (Ri) to ∼0.25 at the northern edge of the anvil were almost entirely a consequence of the MCS-induced westerly outflow jet. The large vertical shear is found to decrease Ri both directly, and by contributing to reductions in static stability near the northern anvil edge through differential advection of (equivalent) potential temperature gradients, which are in turn influenced by adiabatic cooling associated with the mesoscale updraft located upstream within the anvil. On the south side of the MCS, the vertical shear associated with easterly outflow was significantly offset by environmental westerly shear, which resulted in larger Ri and less widespread model turbulent kinetic energy (TKE) than at the northern anvil edge.

An instability index, called the shape factor (SF), for weather forecasting was proposed using temperature profiles from a NASA/LaRC-hosted web-page featuring NOAA Rapid Update Cycle (RUC) radiosonde data used for short-term weather predictions. The index of instability was predicated on the sign and magnitude of the gradient of the equivalent potential temperature. A training data set of temperature profiles was obtained for both clear and severe weather conditions and the SF index was computed. The data showed marked differences in the magnitudes of the shape factors for the two weather conditions. Calculations of SF were compared with the several well-established instability indices and there appeared marked agreement between the SF and these other metrics. It is being proposed that this new index could be used as a parameter for input to statistical weather forecasting models to enhance overall aviation safety and air traffic management efficiency. Of particular relevance is the fact that more comprehensive results can be garnered since the SF index is calculated from the entire temperature profile and not just from selected temperatures as is the case for other indices of instability. Copyright © 2008 Royal Meteorological Society

Numerous techniques exist for measuring airborne delay. These techniques vary in the ways in which airborne delay is modeled and quantified and in how delay is measured. We define a new approach for measuring airborne delay that expands our ability to analyze problems in airspace congestion and the different types of Air Traffic Control action used to manage airborne traffic. We develop mathematical models to detect and record where and when airborne delays occur in the form of vectoring, directs, airspeed controls, and amendments. Airborne delays are computed between consecutive position updates using historical enhanced traffic management system flight data and rapid update cycle winds aloft data. We present the initial results of applying these airborne delay estimation techniques. Finally, we introduce the application of these techniques to analyzing airborne delay propagation and the dependence of airborne delay on weather using various visualization techniques.

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over Colorado, Wyoming, and parts of the surrounding states. The analysis used a 10-km horizontal grid with 21 vertical levels and an hourly temporal resolution. The LAPS archive includes forty-six 1D surface fields and nine 3D upper-air fields, spanning the period 1 September 2001 through 31 August 2003. The RUC20 dataset includes hourly 3D atmospheric analyses over the contiguous United States and parts of southern Canada and northern Mexico, with 50 vertical levels. The RUC20 archive contains forty-six 1D surface fields and fourteen 3D upper-air fields, spanning the period 1 October 2002 through 31 September 2003. The datasets are archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

This study presents results from an experiment conducted to measure the impact of locally initializing a numerical weather prediction model on that model’s ability to predict precipitation and other surface parameters. The study consisted of quantifying the impact of initializing the Weather and Research Forecast (WRF) model with the Advanced Weather Interactive Processing System (AWIPS) Local Analysis and Prediction System (LAPS) diagnostic analyses. In the experiment, WRF was run for two different initial times: 0600 and 1800 UTC. For each initial time, the model was run twice, once using LAPS for the initial conditions, and once using the North American Mesoscale model (NAM; also known as the Eta Model at the time of the experiment). The impact of the local LAPS initialization on the model forecast of surface parameters is presented. Additionally, the model’s quantitative precipitation forecast (QPF) skill is compared for three different model configurations: 1) WRF initialized with LAPS, 2) WRF initialized with NAM, and 3) the standard NAM/Eta Model. The experiment ran from 1 June 2005 to 31 July 2005. Results show that WRF forecasts initialized by LAPS have a more accurate representation of convection in the short range. LAPS-initialized forecasts also offer more accurate forecasts of 2-m temperature and dewpoint, 10-m wind, and sea level pressure, particularly in the short range. Most significantly, precipitation forecasts from WRF runs initialized by LAPS are more accurate than WRF runs initialized by NAM. WRF initialized with LAPS also demonstrates higher QPF skill than does the NAM/Eta Model, particularly in the short range when the precipitation thresholds are higher (0.25 in. in 3 h versus 0.10 in. in 3 h), and when forecasts are initialized at 0600 UTC rather than initialized at 1800 UTC.

This study develops and evaluates a statistical scheme for forecasting warm-season lightning over Florida. Four warm seasons of analysis data from the Rapid Update Cycle (RUC) and lightning data from the National Lightning Detection Network are used in a perfect prognosis technique to develop a high-resolution, gridded forecast guidance product for warm-season cloud-to-ground (CG) lightning over Florida. The most important RUC-derived parameters are used to develop equations producing 3-hourly spatial probability forecasts for one or more CG flashes, as well as the probability of exceeding various flash count percentile thresholds. Binary logistic regression is used to develop the equations for one or more flashes, while a negative binomial model is used to predict the amount of lightning, conditional on one or more flashes occurring. The scheme is applied to output from three mesoscale models during an independent test period (the 2006 warm season). The evaluation is performed using output from the National Centers for Environmental Prediction (NCEP) 13-km RUC (RUC13), the NCEP 12-km North American Mesoscale Model, and local high-resolution runs of the Weather Research and Forecasting (WRF) Model for a domain over south Florida. Forecasts from all three mesoscale models generally show positive skill through the 2100–2359 UTC period with respect to a model containing only climatology and persistence (L-CLIPER) and persistence alone. A forecast example using the high-resolution WRF Model is shown for 16–17 August 2006. Although the exact timing and placement of forecast lightning are not perfect, there generally is good agreement between the forecasts and their verification, with most of the observed lightning occurring within the higher forecast probability contours.

Mesoscale convective systems that affect a limited area within the southern plains of the United States during late morning hours during the warm season are investigated. A climatological study over a 5-yr period documents the initiation locations and times, tracks, associated severe weather, and relation to synoptic features over the lifetimes of 145 systems. An assessment is also made of system evolution in each case during the late morning. For a subset of 48 systems, vertical profiles of basic variables from Rapid Update Cycle (RUC) model analyses are used to characterize the environment of each system. Scatter diagrams and discriminant analyses are used to assess which environmental variables are most promising in helping to determine which of two classes of evolutionary character each system will follow.

Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources. Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.

Accurate cloud-ceiling-height forecasts derived from numerical weather prediction (NWP) model data are useful for aviation and other interests where low cloud ceilings have an impact on operations. A demonstration of the usefulness of data-mining methods in developing cloud-ceiling forecast algorithms from NWP model output is provided here. Rapid Update Cycle (RUC) 1-h forecast data were made available for nearly every hour in 2004. Various model variables were extracted from these data and stored in a database of hourly records for routine aviation weather report (METAR) station KJFK at John F. Kennedy International Airport along with other single-station locations. Using KJFK cloud-ceiling observations as ground truth, algorithms were derived for 1-, 3-, 6-, and 12-h forecasts through a data-mining process. Performance of these cloud-ceiling forecast algorithms, as evaluated through cross-validation testing, is compared with persistence and Global Forecast System (GFS) model output statistics (MOS) performance (6 and 12 h only) over the entire year. The 1-h algorithms were also compared with the RUC model cloud-ceiling (or cloud base) height translation algorithms. The cloud-ceiling algorithms developed through data mining outperformed these RUC model translation algorithms, showed slightly better skill and accuracy than persistence at 3 h, and outperformed persistence at 6 and 12 h. Comparisons to GFS MOS (which uses observations in addition to model data for algorithm derivation) at 6 h demonstrated similar performance between the two methods with the cloud-ceiling algorithm derived through data mining demonstrating more skill at 12 h.

Integrated precipitable water (IPW) estimates derived from time delays in the arrival of global positioning system (GPS) satellite signals are a relatively recent, high-frequency source of atmospheric moisture information available for real-time data assimilation. Different experimental versions of the Rapid Update Cycle (RUC) have assimilated these observations to assess GPS-IPW impact on moisture forecasts. In these tests, GPS-IPW data have proven to be a useful real-time source of moisture information, leading to more accurate short-range moisture forecasts when added to other observations. A multiyear experiment with parallel (one with GPS-IPW processed 24 h after the fact, one without) 3-h cycles using the original 60-km RUC was run from 1999 to 2004 with verification of each cycle against rawinsonde observations. This experiment showed a steady increase in the positive impact in short-range relative humidity (RH) forecasts due to the GPS-IPW data as the number of observing sites increased from 18 to almost 300 (as of 2004) across the United States and Canada. Positive impact from GPS-IPW on 850–700-hPa RH forecasts was also evident in 6- and 12-h forecasts. The impact of GPS-IPW data was also examined on forecasts from the more recent 20-km RUC, including a 1-h assimilation cycle and improved assimilation and physical parameterizations, now using real-time GPS-IPW retrievals available 30 min after valid time. In a 3-month comparison during the March–May 2004 period, 20-km RUC cycles with and without assimilation of GPS-IPW were compared with IPW for 3-, 6-, 9-, and 12-h forecasts. Using this measure, assimilation of GPS-IPW data led to the strongest improvements in the 3- and 6-h forecasts and smaller but still evident improvements in 9- and 12-h forecasts. In a severe convective weather case, inclusion of GPS-IPW data improved forecasts of convective available potential energy, an important predictor of severe storm potential, and relative humidity. Positive impact from GPS-IPW assimilation was found to vary over season, geographical location, and time of day, apparently related to variations in vertical mixing. For example, GPS-IPW has a stronger effect on improving RH forecasts at 850 hPa at nighttime (than daytime) and in cooler seasons (than warmer seasons) when surface moisture observations are less representative of conditions aloft. As a result of these studies, assimilation of GPS-IPW was added to the operational RUC run at NOAA/NCEP in June 2005 and to the operational North American Mesoscale model (also at NCEP) in June 2006 to improve their accuracy for short-range moisture forecasts.

A time-lagged ensemble forecast system is developed using a set of hourly initialized Rapid Update Cycle model deterministic forecasts. Both the ensemble-mean and probabilistic forecasts from this time-lagged ensemble system present a promising improvement in the very short-range weather forecasting of 1–3 h, which may be useful for aviation weather prediction and nowcasting applications. Two approaches have been studied to combine deterministic forecasts with different initialization cycles as the ensemble members. The first method uses a set of equally weighted time-lagged forecasts and produces a forecast by taking the ensemble mean. The second method adopts a multilinear regression approach to select a set of weights for different time-lagged forecasts. It is shown that although both methods improve short-range forecasts, the unequally weighted method provides the best results for all forecast variables at all levels. The time-lagged ensembles also provide a sample of statistics, which can be used to construct probabilistic forecasts.

The performance of an ensemble forecasting system initialized using varied soil moisture alone has been evaluated for rainfall forecasts of six warm season convective cases. Ten different soil moisture analyses were used as initial conditions in the ensemble, which used the Weather Research and Forecasting (WRF) Advanced Research WRF (ARW) model at 4-km horizontal grid spacing with explicit rainfall. Soil moisture analyses from the suite of National Weather Service operational models—the Rapid Update Cycle, the North American Model (formerly known as the Eta Model), and the Global Forecasting System—were used to design the 10-member ensemble. For added insight, two other runs with extremely low and high soil moistures were included in this study. Although the sensitivity of simulated 24-h rainfall to soil moisture was occasionally substantial in both weakly forced and strongly forced cases, a U-shaped rank histogram indicated insufficient spread in the 10-member ensemble. This result suggests that ensemble forecast systems using soil moisture perturbations alone might not add enough variability to rainfall forecasts. Perturbations to both atmospheric initial conditions and land surface initial conditions as well as perturbations to other aspects of model physics may increase forecast spread. Correspondence ratio values for the 0.01- and 0.5-in. rainfall thresholds imply some spread in the soil moisture ensemble, but mainly in the weakly forced cases. Relative operating characteristic curves for the 10-member ensemble and for various rainfall thresholds indicate modest skill for all thresholds with the most skill associated with the lowest rainfall threshold, a result typical of warm season events.

Using two general linear regression models, we compared the ability of the aerosol optical thickness (AOT) retrieved by the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to predict ground-level PM2.5 concentrations in St. Louis, MO and its surrounding areas . The models included meteorological parameters obtained from the National Oceanic and Atmospheric Administration (NOAA)'s Rapid Update Cycle (RUC20) model as covariates. Both MISR and MODIS AOT values were highly significant predictors of PM2.5 concentrations. The MISR and MODIS models have overall comparable predictability of ground-level PM2.5 concentrations. The MISR model explained a slightly greater percentage (62%) of the variability in PM2.5 concentrations than the MODIS model (51%), and thus was a better fit. Over the entire data range, the MISR model underpredicts PM2.5 concentrations by approximately 12%, whereas the MODIS model underpredicts PM2.5 concentrations by approximately 18%. This underestimation occurred primarily at higher PM2.5 concentrations in both models. The regression coefficients from two models were highly comparable, suggesting that combining MISR and MODIS AOT data might benefit from the higher predicting accuracy of MISR and the better spatial coverage of MODIS. The newly developed particle size/shape indicators in MISR and MODIS aerosol product did not significantly improve our ability to predict PM2.5 concentrations using AOT measurements. Finally, using hourly PM2.5 concentrations did not seem to improve its association with AOT for the current study region.

Modern Numerical Weather Prediction (NWP) models make use of the GNSS-derived Zenith Total Delay (ZTD) or Integrated Water Vapour (IWV) estimates to enhance the quality of their forecasts. Usually, the ZTD is assimilated into the NWP models on 3- hourly to 6-hourly intervals but with the advancement of NWP models towards higher update rates e.g. 1-hourly cycling in the Rapid Update Cycle (RUC) NWP, it has become of high interest to estimate ZTD on sub-hourly intervals. In turn, this imposes requirements related to the timeliness and accuracy of the ZTD estimates and has lead to a development of various strategies to process GNSS observations to obtain ZTD with different latencies and accuracies. Using present GNSS products and tools, ZTD can be estimated in real-time (RT), near real-time (NRT) and post-processing (PP) modes. The aim of this study is to provide an overview and accuracy assessment of various RT, NRT, and PP IWV estimation systems and comparing their achieved accuracy with the user requirements for GNSS meteorology. The NRT systems are based on Bernese GPS Software 5.0 and use a double-differencing strategy whereas the PP system is based on the Bernese GNSS Software 5.2 using the precise point positioning (PPP) strategy. The RT systems are based on the BKG Ntrip Client 2.7 and the PPPWizard both using PPP. The PPP-Wizard allows integer ambiguity resolution at a single station and therefore the effect of fixing integer ambiguities on ZTD estimates will also be presented.

The Weather Research and Forecasting Model (WRF) is a numerical weather prediction model maintained by the National Center for Atmospheric Research (NCAR). The US Army Research Laboratory (ARL) has developed the Weather Running Estimate Nowcast (WRE N) model based on the Weather Research and Forecasting Model Advanced Research WRF (WRF ARW) to predict small-scale phenomena of interest to Warfighters, such as mountain/valley breezes and land/sea breezes. NCAR has developed the Model Evaluation Tools (MET) to evaluate the accuracy of WRF forecasts using observations of meteorological variables. The traditional use of MET calculates model performance over the entire model domain. High-resolution modeling requires more focused verification at subdomain levels. A Geographical Information System (GIS) enables consideration of terrain variables in model assessments using a location-based approach. We discuss our GIS approach to verify WRF-ARW with a 1-kilometer horizontal resolution inner domain centered near San Diego, California. We selected 5 case study days from February and March of 2012. The literature indicated that elevation is strongly correlated with meteorological parameters, which became our focus. We found that elevation accounts for a significant portion of the variance in the model error. The study demonstrated that a GIS can analyze spatially distributed point forecast errors over subdomains, to reveal the dependence of forecast errors on elevation.

Computer models are often deterministic simulators used to predict several environmental phenomena. Such models do not associate any measure of uncertainty with their output since they are derived from deterministic specifications. However, many sources of uncertainty exist in constructing and employing numerical models. We are motivated by temperature maps arising from the Rapid Update Cycle (RUC) model, a regional short-term weather forecast model for the continental United States (US) which provides forecast maps without associated uncertainty. Despite a rapidly growing literature on uncertainty quantification, there is little regarding statistical methods for attaching uncertainty to model output without information about how deterministic predictions are created. Although numerical models produce deterministic surfaces, the output is not the ‘true’ value of the process and, given the true value and the model output, the associated error is not stochastic. However, under suitable stochastic modeling, this error can be interpreted as a random unknown. Then, we infer about this error using a Bayesian specification within a data fusion setting, fusing the computer model data with some external validation data collected independently over the same spatial domain. Illustratively, we apply our modeling approach to obtain an uncertainty map associated with RUC forecasts over the northeastern US.

Experimental gridded forecasts of surface temperature issued by National Weather Service offices in the western United States during the 2003/04 winter season (18 November 2003–29 February 2004) are evaluated relative to surface observations and gridded analyses. The 5-km horizontal resolution gridded forecasts issued at 0000 UTC for forecast lead times at 12-h intervals from 12 to 168 h were obtained from the National Digital Forecast Database (NDFD). Forecast accuracy and skill are determined relative to observations at over 3000 locations archived by MesoWest. Forecast quality is also determined relative to Rapid Update Cycle (RUC) analyses at 20-km resolution that are interpolated to the 5-km NDFD grid as well as objective analyses obtained from the Advanced Regional Prediction System Data Assimilation System that rely upon the MesoWest observations and RUC analyses. For the West as a whole, the experimental temperature forecasts issued at 0000 UTC during the 2003/04 winter season exhibit skill at lead times of 12, 24, 36, and 48 h on the basis of several verification approaches. Subgrid-scale temperature variations and observational and analysis errors undoubtedly contribute some uncertainty regarding these results. Even though the “true” values appropriate to evaluate the forecast values on the NDFD grid are unknown, it is estimated that the root-mean-square errors of the NDFD temperature forecasts are on the order of 3°C at lead times shorter than 48 h and greater than 4°C at lead times longer than 120 h. However, such estimates are derived from only a small fraction of the NDFD grid boxes. Incremental improvements in forecast accuracy as a result of forecaster adjustments to the 0000 UTC temperature grids from 144- to 24-h lead times are estimated to be on the order of 13%.