Numerical Forecast Models Research Articles

Estimation of urban mixed layer height in Zanjan using LIDAR observations and numerical modeling

Air pollution predictions often require the height of atmospheric mixed layer in time especially in big cities. Here, the variation of the height of this layer is estimated from direct measurements and also from a numerical forecast model with a high resolution boundary layer scheme. The height of the daytime mixed layer for the city of Zanjan (48.5°N, 36.7°E, 1700m above sea level) is measured using a LIDAR (532 nm) system, which works based on aerosols scattering of laser light. The mixed layer height (z i ) for Zanjan city, well above mean sea level compared to other major cities in the world, is found to be between 1.4 km typically in spring and 2.2 km in summer, for synoptic calm conditions. Also, the MM5 forecast model with a proper boundary layer scheme (MRF) is used to estimate z i which shows rather good agreement with direct observations using the LIDAR system. The entrainment zone of the mixed layer was also found to undergo some occasional temporal growth that may be attributed to shear instability that led to more mixed layer growth.

Removal of Systematic Model Bias on a Model Grid

Abstract Virtually all numerical forecast models possess systematic biases. Although attempts to reduce such biases at individual stations using simple statistical corrections have met with some success, there is an acute need for bias reduction on the entire model grid. Such a method should be viable in complex terrain, for locations where gridded high-resolution analyses are not available, and where long climatological records or long-term model forecast grid archives do not exist. This paper describes a systematic bias removal scheme for forecast grids at the surface that is applicable to a wide range of regions and parameters. Using observational data and model forecasts over the Pacific Northwest, a method was developed to reduce the biases in gridded 2-m temperature, 2-m dewpoint temperature, and 12-h precipitation forecasts. The method first estimates bias at observing locations using errors from forecasts that are similar to the current forecast. These observed biases are then used to estimate bias on the model grid by pairing model grid points with stations that have similar elevation and/or land-use characteristics. Results show that this approach reduces bias substantially, particularly for periods when biases are large. Adaptations to weather regime changes are made within a short period, and the method essentially “shuts off” when model biases are small. With modest modifications, this approach can be extended to additional variables.

A Verification of Numerical Model Forecasts for Sounding-Derived Indices above Udine, Northeast Italy

Abstract In this work, 40 different indices derived from real soundings and the corresponding ECMWF model forecasts for the same location (near Udine, northeast Italy) are compared. This comparison is repeated for more than 500 days, from June 2004 to December 2005. The comparison is performed in terms of linear correlation, bias, root-mean-square error, and relative absolute error. The results show that the quality of agreement varies considerably among the different indices and lead times. It is found that only 15 indices have strong correlations (R2 always larger than 0.5) that hold up until the fourth forecast day (+96 h). The indices having the best agreement are usually representative of the thermal fields while those having the worst agreement are usually representative of the wind fields, particularly in the low levels. It is also found that the correlations for all 40 of the indices change for the 4 times of the day (one sounding every 6 h), with the 1200 UTC comparison being usually the best one. Also, at 1800 and 0000 UTC the potential instability is strongly underestimated by the model. To test if these errors depend on the vertical resolutions, a similar comparison has been done between the observed sounding reduced in the World Meteorological Organization TEMP format (as assimilated by the NWP model) and the originals at high vertical resolution. It is found that the indices derived from the TEMP format vary from those with the highest resolution, in particular the indices that depend on the details of the low-level profile, such as the level of free convection and the initial height of the lifted parcel. Nevertheless, only the shear has a very low correlation (R2 = 0.15) with the corresponding index derived from the high-resolution soundings, because it is very sensitive to the number of vertical levels used.

Source area of spring population of meadow moth, Loxostege sticticalis L. (Lepidoptera: Pyralidae), in Northeast China

The meadow moth, Loxostege sticticalis L., is an important outbreak pest in Northeast China. The spring population in Northeast China was considered as immigrants from North China; however, the population dynamics during 1996–2007 was not consistent with this traditional theory. In this study, the source area of the spring population of the meadow moth in Northeast China was identified by trajectory analysis using the wind field simulated by mesoscale numerical forecast model MM5. The results indicated that the population of the meadow moth in North China was able to migrate into Northeast China, but had not made a crucial contribution to the outbreaks there since the weather systems were generally adverse for such a long range migration in most of the years. On the other hand, high density cocoons of L. sticticalis have frequently been found in autumn in Northeast China since 1996. The trajectory analyses indicated that most of L. sticticalis in Northeast China in spring emerged from the cocoons that had overwintered either locally or in neighboring countries, i.e. Mongolia and Russia. The development of overwintering areas at higher latitudes coincides with the increased frequency of second-generation larvae; both phenomena might be attributed to the regional climate change associated with global warming.

Athabasca River ice jam formation and release events in 2006 and 2007

Abstract River ice jams often lead to disastrous floods, since the sudden failure of a major jam is like a small-scale dam break, producing violent ice runs and rapidly rising water levels. Although the resulting flood wave attenuates as it travels downstream, water levels can rebuild again if the ice run is stalled even briefly. Because of the rapid development of high water, it is highly desirable to be able to forecast ice jam events. However, the advancement of knowledge of these events is limited by the lack of reliable field data. Such data is also needed for validation of numerical forecasting models. This paper presents details of the field observations of ice jam events carried out in 2006 and 2007 on the Athabasca River at Fort McMurray, Alberta (AB), Canada. The river breakup in both years was quite dynamic, with extensive ice runs, as well as ice jam formation and release events. Quantitative data describing the speed, height and shape of ice jam release waves and the celerity of breaking and shoving fronts, as well as qualitative data describing the ice conditions, provide unique and valuable data contributing towards a better understanding of the dynamics of ice jam formation and release processes. Through application of numerical ice jam release modeling, wave propagation, ice resistance effects and ice–water interactions are explored using these data.

Interpreting adjoint and ensemble sensitivity toward the development of optimal observation targeting strategies

Two general methods, adjoint or singular vector methods, and ensemble-based methods, have been previously investigated to identify locations where observations would have a significant positive impact on a numerical weather model forecast. In this paper, we perform a basic comparison of targeting regions chosen to reduce the expected variance of a chosen forecast response function within an ensemble Kalman filter (EnKF) based on both an adjoint method and an ensemble method. Ensemble sensitivity is defined by linear regressions of a chosen forecast response function onto the model initial-time state variables, and is used to calculate variance reduction fields to provide targeting guidance for the ensemble-based method. Adjoint sensitivity is used to provide targeting guidance for the adjoint-based method. 90 ensemble forecasts are considered over a 24-hour forecast period, and the response function is chosen to represent the sea-level pressure at a single point in the Pacific Northwest United States. Targeting by ensemble guidance is shown to be a function of ensemble sensitivity and both the initial-time model state and observation variance. We find that large areas of variance reduction exist away from regions of large ensemble sensitivity, adjoint sensitivity, and the initial-time variance of the model state. For hypothetical aircraft observations, ensemble guidance is superior to adjoint guidance for 850 hPa temperature observations in a single case. This advantage increases as the number of flight tracks increases. In all cases, as more flight tracks are considered, diminishing returns on response function variance reduction are realized. Implications of these results for the development of targeting strategies are discussed.

An advanced model for predicting clear‐air visibility

AbstractThis article describes a new model to predict clear‐air visibility as a function of bearing with respect to the Sun. Its primary use is for aviation forecasting, and it is capable of providing estimates of visual range for any point along an aircraft flight path, using normal output from a numerical forecast model. It is also capable of forecasting line‐of‐sight visibility along airport approach paths. The model uses Mie theory to predict scattering from the direct solar beam into the line of sight, and combines this with a diffuse and Rayleigh component of the extinction coefficient to provide visual range estimates. The methodology is presented together with some examples of how visibility varies with solar bearing angle. © Crown copyright 2007. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd

Evaluation of Dispersion Forecasts Driven by Atmospheric Model Output at Coarse and Fine Resolution

Abstract Lagrangian parcel models are often used to predict the fate of airborne hazardous material releases. The atmospheric input for these integrations is typically supplied by surrounding surface and upper-air observations. However, situations may arise in which observations are unavailable and numerical model forecasts may be the only source of atmospheric data. In this study, the quality of the atmospheric forecasts for use in dispersion applications is investigated as a function of the horizontal grid spacing of the atmospheric model. The Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) was used to generate atmospheric forecasts for 14 separate Dipole Pride 26 trials. The simulations consisted of four telescoping one-way nested grids with horizontal spacings of 27, 9, 3, and 1 km, respectively. The 27- and 1-km forecasts were then used as input for dispersion forecasts using the Hazard Prediction Assessment Capability (HPAC) modeling system. The resulting atmospheric and dispersion forecasts were then compared with meteorological and gas-dosage observations collected during Dipole Pride 26. Although the 1-km COAMPS forecasts displayed considerably more detail than those on the 27-km grid, the RMS and bias statistics associated with the atmospheric observations were similar. However, statistics from the HPAC forecasts showed the 1-km atmospheric forcing produced more accurate trajectories than the 27-km output when compared with the dosage measurements.

Use of MODIS and synthetic aperture radar wind speed imagery to describe the morphology of open cell convection

Moderate resolution imaging spectroradiometer (MODIS) imagery, meteorological analyses, and numerical weather prediction model forecasts for a 28–29 October 2006 case study are employed to argue that squall-and-lull patterns observed within a corresponding synthetic aperture radar (SAR) derived wind speed image are the result of open cell convection. In addition, the suite of data is exploited to advance a new conceptual model of open cell convection. MODIS imagery reveals that the open cell convection reported in this paper is composed primarily of cumulus humulus and cumulus congestus clouds, with cloud shadow measurements yielding a factor-of-three variation in cloud top height between the highest clouds (those on the leading or downwind edge of the cells) and the more shallow clouds along the sides and rear of the cells. The MODIS imagery reveals that the tallest clouds typically contain ice, which suggests that precipitation-driven downdrafts may cause the wind squalls seen in SAR-derived wind speed imagery. Individual squall-and-lull features have an elliptical boundary with an arc-shaped squall along the downwind edge of the feature, with wind speed decreasing gradually across the feature to its upwind edge.

MedArgo: a drifting profiler program in the Mediterranean Sea

Abstract. In the framework of the EU-funded MFSTEP project, autonomous drifting profilers were deployed throughout the Mediterranean Sea to collect temperature and salinity profile data and to measure subsurface currents. The realization of this profiler program in the Mediterranean, referred to as MedArgo, is described and assessed using data collected between June 2004 and December 2006 (including more than 2000 profiles). Recommendations are provided for the permanent future implementation of MedArgo in support of operational oceanography in the Mediterranean Sea. More than twenty drifting profilers were deployed from research vessels and ships-of-opportunity in most areas of the Mediterranean. They were all programmed to execute 5-day cycles with a drift at a parking depth of 350 m and CTD profiles from either 700 or 2000 m up to the surface. They stayed at the sea surface for about 6 h to be localised by, and transmit the data to, the Argos satellite system. The temperature and salinity data obtained with pumped Sea-Bird CTD instruments were processed and made available to the scientific community and to operational users in near-real time using standard Argo protocols, and were assimilated into Mediterranean numerical forecasting models. In general, the cycling and sampling characteristics chosen for the MedArgo profilers were found to be adequate for the Mediterranean. However, it is strongly advised to use GPS and global cellular phone telemetry or the future Argos bi-directional satellite system in order to avoid data compression and losses, for the continuation of the Mediterranean drifting profiler program.

Early emergency response by means of dispersion forecasting – emergency management of the Deutscher Wetterdienst in the context of national and international agreements

Abstract Based on results of the numerical weather forecast models; the DWD (Deutscher Wetterdienst, German Meteorological Service) is immediately able to calculate dispersion forecasts in the case of an emergency with an emission of radioactive inventory. This information is essential for the management of further countermeasures. There are legal agreements between the federal states of Germany and the Federal Government regulating the mutual aid. For the emergency preparedness system RODOS (Real Time Online Decision Support) the results of the numerical weather forecasts are provided and complemented by medium-range dispersion forecasts. In the framework of the German computer network IMIS (Integrated Measurement and Information System for the Surveillance of Radioactivity in the Environment) the results of trajectory calculations are presented graphically within 10 minutes, those from the LPDM (Lagrangian Particle Dispersion Model) forecasts within 30 minutes. In case of a nuclear emergency, global distribution of information via the WMO (World Meteorological Organization) communication system GTS is part of international agreements between IAEA (International Atomic Energy Agency) and WMO, thus giving the DWD a key role. The commitments of the DWD in managing nuclear emergencies in the framework of national and international agreements are presented, as well as internal emergency arrangements.

Consensus of Numerical Model Forecasts of Significant Wave Heights

Abstract The operational consensus forecast (OCF) scheme uses past performance to bias correct and combine numerical forecasts to produce an improved forecast at locations where recent observations are available. Here, OCF uses past observations and forecasts of significant wave height from five numerical wave models available in real time at the Australian Bureau of Meteorology. In addition to OCF, different adaptive weighting and forecast combination strategies are investigated. At deep-water sites (ocean depth > 25 m), all of the interpolated raw model forecasts outperformed 24-h persistence and, after bias correction, one model was clearly best. Significant improvements over raw model significant wave height forecasts were achieved by bias correction, linear-regression methods, and combination strategies. The best forecasts were obtained from a “composite of composites” in which models with highly correlated errors were combined before being included in the performance-weighted bias-corrected forecast. This technique slightly outperformed the linear-regression-corrected best model. At shallow-water sites (ocean depth < 25 m), all raw models perform poorly relative to the 24-h persistence. The composited, corrected forecasts significantly improved on raw model significant wave height forecasts but only slightly outperformed the 24-h persistence. The raw models generated unrealistically large biases that tended to be amplified with larger observed values of significant wave height.

NCAR/CU Surface, Soil, and Vegetation Observations during the International H2O Project 2002 Field Campaign

The May–June 2002 International H2O Project was held in the U.S. Southern Great Plains to determine ways that moisture data could be collected and utilized in numerical forecast models most effectively. We describe the surface and boundary layer components, and indicate how the data can be acquired. These data document the eddy transport of heat and water vapor from the surface to the atmosphere (in terms of sensible heat flux H and latent heat flux LE), as well as radiative, atmospheric, soil, and vegetative factors that affect it, so that the moisture and heat supply to the atmosphere can be related to surface properties both for observational studies and tests of land surface models. The surface dataset was collected at 10 surface flux towers at locations representing the major types of land cover and extending from southeast Kansas to the Oklahoma Panhandle. At each location, the components of the surface energy budget (H, LE, net radiation, and soil heat flux) are documented each half-hour, along with the weather (wind, temperature, mixing ratio, air pressure, and precipitation), soil temperature, moisture, and matric potential down to 70–90 cm beneath the surface at 9 of the 10 sites. Observations of soil and vegetation properties and their horizontal changes were taken near all 10 towers during periodic visits. Aircraft measurements of H and LE from repeated low-level flight tracks along three tracks collocated with the surface sites extend the flux tower measurements horizontally. We illustrate the effects of vegetation and soil moisture on the H and LE and their horizontal variability.

Duration of an ionospheric data assimilation initialization of a coupled thermosphere‐ionosphere model

Initial conditions provide a critical input for accurate numerical forecast models in meteorology and oceanography. In this paper, we address this problem in space weather forecast models of the thermosphere‐ionosphere system by using the electron densities from the Global Assimilation of Ionospheric Measurements (GAIM) model to initialize the ionospheric part of the Thermosphere Ionosphere Nested Grid (TING) model. The electron densities from the GAIM‐initialized TING model (G‐TING) are compared with the output from the stand‐alone TING model (S‐TING) for geomagnetically quiet and disturbed times in the early April 2004 period in order to observe how long the effects of the initialization would last. Our study shows that the e‐folding time of the initialization is about 2 ∼ 3 hours for most conditions, although this result would probably be different if the initialization for the thermosphere is also included. However, this relaxation time displays significant variations with latitude, local time, and height, and it may also depend on the initial electron density differences between G‐TING and S‐TING. Furthermore, positive (G‐TING > S‐TING) and negative (G‐TING < S‐TING) density differences have different time durations of the initialization effects. Our study also indicates that there is little variation of the relaxation time with the geomagnetic activity despite the impact of geomagnetic storms on the thermosphere‐ionosphere system.

The Hurricane Rainband and Intensity Change Experiment: Observations and Modeling of Hurricanes Katrina, Ophelia, and Rita

The Hurricane Rainband and Intensity Change Experiment (RAINEX) used three P3 aircraft aided by high-resolution numerical modeling and satellite communications to investigate the 2005 Hurricanes Katrina, Ophelia, and Rita. The aim was to increase the understanding of tropical cyclone intensity change by interactions between a tropical cyclone's inner core and rainbands. All three aircraft had dual-Doppler radars, with the Electra Doppler Radar (ELDORA) on board the Naval Research Laboratory's P3 aircraft, providing particularly detailed Doppler radar data. Numerical model forecasts helped plan the aircraft missions, and innovative communications and data transfer in real time allowed the flights to be coordinated from a ground-based operations center. The P3 aircraft released approximately 600 dropsondes in locations targeted for optimal coordination with the Doppler radar data, as guided by the operations center. The storms were observed in all stages of development, from tropical depression to category 5 hurricane. The data from RAINEX are readily available through an online Field Catalog and RAINEX Data Archive. The RAINEX dataset is illustrated in this article by a preliminary analysis of Hurricane Rita, which was documented by multiaircraft flights on five days 1) while a tropical storm, 2) while rapidly intensifying to a category 5 hurricane, 3) during an eye-wall replacement, 4) when the hurricane became asymmetric upon encountering environmental shear, and 5) just prior to landfall.

Prediction of Near-Surface Variables at Independent Locations from a Bias-Corrected Ensemble Forecasting System

Abstract The ability of a multimodel short-range bias-corrected ensemble (BCE) forecasting system, created as part of NOAA’s New England High Resolution Temperature Program during the summer of 2004, to obtain accurate predictions of near-surface variables at independent locations within the model domain is explored. The original BCE approach produces bias-corrected forecasts only at National Weather Service (NWS) observing surface station locations. To extend this approach to obtain bias-corrected forecasts at any given location, an extended BCE technique is developed and applied to the independent observations provided by the Oklahoma Mesonet. First, a Cressman weighting scheme is used to interpolate the bias values of 2-m temperature, 2-m dewpoint temperature, and 10-m wind speeds calculated from the original BCE approach at the NWS observation station locations to the Oklahoma Mesonet locations. These bias values are then added to the raw numerical model forecasts bilinearly interpolated to this same specified location. This process is done for each forecast member within the ensemble and at each forecast time. It is found that the performance of the extended BCE is very competitive with the original BCE approach across the state of Oklahoma. Therefore, a simple postprocessing scheme like the extended BCE system can be used as part of an operational forecasting system to provide reasonably accurate predictions of near-surface variables at any location within the model domain.

The Sensitivity of Numerical Forecasts to Convective Parameterization: A Case Study of the 17 February 2004 East Coast Cyclone

Abstract The sensitivity of numerical model forecasts of coastal cyclogenesis and frontogenesis to the choice of model cumulus parameterization (CP) scheme is examined for the 17 February 2004 southeastern U.S. winter weather event. This event featured a complex synoptic and mesoscale environment, as the presence of cold-air damming, a developing coastal surface cyclone, and an upper-level trough combined to present a daunting winter weather forecast scenario. The operational forecast challenge was further complicated by erratic numerical model predictions. The most poignant area of disagreement between model runs was the treatment of a coastal cyclone and an associated coastal front, features that would affect the location and timing of precipitation and influence the precipitation type. At the time of the event, it was hypothesized that the Betts–Miller–Janjić (BMJ) CP scheme was dictating the location and intensity of the initial coastal cyclone center in operational Eta Model forecasts. For this reason, forecasts for this case were rerun with the workstation Eta Model using the Kain–Fritsch (KF) CP scheme to further examine the sensitivity to this parameterization choice. Results confirm that the model CP scheme played a major role in the forecast for this case, affecting the quantitative precipitation forecast as well as the strength, location, and structure of coastal cyclogenesis and coastal frontogenesis. The Eta Model forecast using the KF CP scheme produced a relatively uniform distribution of convective precipitation oriented along the axis of an inverted trough and strong coastal front. In contrast, the BMJ forecasts resulted in a weaker coastal front and the development of multiple distinct closed cyclonic circulations in association with more localized convective precipitation centers. An additional BMJ forecast in which the shallow mixing component of the scheme was disabled bore a closer semblance to the KF forecasts relative to the original BMJ forecast. Suggestions are provided to facilitate the identification of CP-driven cyclones using standard operational model output parameters.

A three-dimensional variational single-Doppler velocity retrieval method with simple conservation equation constraint

Summary In this paper, a new three-dimensional variational analysis scheme capable of retrieving three-dimensional winds from single Doppler observations of convective storms is developed. The method incorporates, in a single cost function, Doppler radar observations, a background field, smoothness and mass continuity constraints, and the residual of reflectivity or radial velocity conservation. By minimizing this cost function, an analysis with the desired fit to these constraints is obtained in a single procedure. In tests with both simulated and real thunderstorm cases, detailed structures of the storms are well retrieved in comparison with reference analysis. Unlike most kinematic retrieval methods, our scheme is capable of directly dealing with data voids. When an analysis background is available, say from a proximity sounding, a wind profiler, or a numerical model forecast, the method naturally blends Doppler radar observations with it. Thus, a smooth transition is obtained between data-rich and datavoid areas. These features, among others, are important if the analysis is to be used to initialize storm-scale numerical models or for diagnostic studies of storm structures.

An Integrated Approach to Mid- and Upper-Level Turbulence Forecasting

Abstract An automated procedure for forecasting mid- and upper-level turbulence that affects aircraft is described. This procedure, termed the Graphical Turbulence Guidance system, uses output from numerical weather prediction model forecasts to derive many turbulence diagnostics that are combined as a weighted sum with the relative weights computed to give best agreement with the most recent available turbulence observations (i.e., pilot reports of turbulence or PIREPs). This procedure minimizes forecast errors due to uncertainties in individual turbulence diagnostics and their thresholds. Thorough statistical verification studies have been performed that focused on the probabilities of correct detections of yes and no PIREPs by the forecast algorithm. Using these statistics as a guide, the authors have been able to intercompare individual diagnostic performance, and test various diagnostic threshold and weighting strategies. The overall performance of the turbulence forecast and the effect of these strategies on performance are described.

On the Influence of Assumed Drop Size Distribution Form on Radar-Retrieved Thunderstorm Microphysics

Abstract Polarimetric radar measurements are used to retrieve drop size distributions (DSD) in subtropical thunderstorms. Retrievals are made with the single-moment exponential drop size model of Marshall and Palmer driven by radar reflectivity measurements and with a two-parameter constrained-gamma drop size model that utilizes reflectivity and differential reflectivity. Results are compared with disdrometer observations. Retrievals with the constrained-gamma DSD model gave better representation of total drop concentration, liquid water content, and drop median volume diameter and better described their natural variability. The Marshall–Palmer DSD model, with a fixed intercept parameter, tended to underestimate the total drop concentration in storm cores and to overestimate significantly the concentration in stratiform regions. Rainwater contents in strong convection were underestimated by a factor of 2–3, and drop median volume diameters in stratiform rain were underestimated by 0.5 mm. To determine possible DSD model impacts on numerical forecasts, evaporation and accretion rates were computed using Kessler-type parameterizations. Rates based on the Marshall–Palmer DSD model were lower by a factor of 2–3 in strong convection and were higher by about a factor of 2 in stratiform rain than those based on the constrained-gamma model. The study demonstrates the potential of polarimetric radar measurements for improving the understanding of precipitation processes and microphysics parameterization in numerical forecast models.