bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dynamics of the Low-Level Jet off the West Coast of Subtropical South America
Authors: Muñoz Ricardo C. , Garreaud RenéD. 
Published: January 2006

Abstract
The subtropical west coast of South America is under the influence of the southeast Pacific anticyclone year-round, which induces persistent southerly winds along the coast of north-central Chile. These winds often take the form of a low-level coastal jet, in many aspects similar to the coastal jet existing off the California coast. Extensive diagnostics of mesoscale model results for a case in October 2000 are used here to describe the mean momentum budget supporting the coastal jet. The jet appears to occur when midlatitude synoptic conditions induce a northerly directed pressure gradient force along the coast of north-central Chile. The very steep coastal terrain precludes the development of a significant easterly low-level wind that would geostrophically balance the pressure gradient. Instead, the meridional flow accelerates until turbulent friction in the marine boundary layer balances the meridional pressure gradient. The resulting force balance is semigeostrophic, with geostrophy valid only in the zonal (cross shore) direction. At higher levels, the topographic inhibition of the easterlies relaxes, and a small easterly flow ensues, which turns out to be very important in the temperature and stability budgets of the layer capping the marine boundary layer.
Atmosphere–Sea Ice Interactions during a Cyclone Passage Investigated by Using Model Simulations and Measurements
Authors: Dierer S. , Schlünzen K. H. , Birnbaum G. , Brümmer B. , Müller G. 
Published: January 2006

Abstract
A high-resolution atmosphere–sea ice model is used to investigate the interactions between cyclones and sea ice cover in polar regions. For this purpose, a cyclone passage observed during the 1999 Fram Strait Cyclone Experiment (FRAMZY) is simulated for two consecutive days. The results of the coupled mesoscale transport and stream model–mesoscale sea ice model (METRAS–MESIM) are compared with aircraft and ice drift measurements. With the exception of temperature, all atmospheric parameters are well simulated. Main reasons for discrepancies were found in large differences between the measurements and the forcing data taken from the results of the regional model (REMO). In addition, advection was slightly wrong as a result of a 17° deviation in wind directions. The altogether well simulated wind field is interactively used to force the sea ice model MESIM; results agree well with drift buoy measurements. Average deviations of simulated and measured ice drift are smaller than 8° for direction and smaller than 3.7 cm s−1 for speed, which is less than 10% of the average speed. The simulated ratio between ice drift and wind velocity increases slightly during cyclone passage from 2.6% to 2.9%, a tendency also known from observations. During a 36-h period, the simulated sea ice concentration locally decreases up to 20% in accordance with measurements. A neglect of changing sea ice cover causes a decrease of the heat flux to the atmosphere from 53 to 12 W m−2. The values correspond to averages over the evaluation region (approximately 228 000 km2) and period (36 h). Temperature and humidity are decreased by 2 K and 0.2 g kg−1, respectively, over the ice-covered region. In contrast, the effect on pressure and wind remains small, probably because the cyclone does not move in the vicinity of the ice edge.
Reply
Authors: Krasnopolsky Vladimir M. , Fox-Rabinovitz Michael S. , Chalikov Dmitry V. 
Published: January 2006

Abstract
This reply is aimed at clarifying and further discussing the methodological aspects of this neural network application for a better understanding of the technique by the journal readership. The similarities and differences of two approaches and their areas of application are discussed. These two approaches outline a new interdisciplinary field based on application of neural networks (and probably other modern machine or statistical learning techniques) to significantly speed up calculations of time-consuming components of atmospheric and oceanic numerical models.
More Observations of Small Funnel Clouds and Other Tubular Clouds
Authors: Bluestein Howard B. 
Published: January 2006

Abstract
In this brief contribution, photographic documentation is provided of a variety of small, tubular-shaped clouds and of a small funnel cloud pendant from a convective cloud that appears to have been modified by flow over high-altitude mountains in northeast Colorado. These funnel clouds are contrasted with others that have been documented, including those pendant from high-based cumulus clouds in the plains of the United States. It is suggested that the mountain funnel cloud is unique in that flow over high terrain is probably responsible for its existence; other types of small funnel clouds are seen both over elevated, mountainous terrain and over flat terrain at lower elevations.
NWP Grid Editing at the Met Office
Authors: Carroll E. B. , Hewson T. D. 
Published: January 2006

Abstract
Over the past few years the way in which central forecast guidance is disseminated by forecasters in the Met Office headquarters has been changing, with an increasing reliance on modification of variables’ output from NWP models. The editing of grids of forecast data at the Met Office Operations Centre in Exeter is described, and two case studies are presented. Results of verification of modified versus raw fields are shown, presenting the concept of “lead time gain” as a unifying measure of relative forecast accuracy. At all lead times net lead time gain outweighs time spent considering and effecting modifications.
Ground Fog Detection from Space Based on MODIS Daytime Data—A Feasibility Study
Authors: Bendix Jörg , Thies Boris , Cermak Jan , Nauß Thomas 
Published: January 2006

Abstract
The distinction made by satellite data between ground fog and low stratus is still an open problem. A proper detection scheme would need to make a determination between low stratus thickness and top height. Based on this information, stratus base height can be computed and compared with terrain height at a specific picture element. In the current paper, a procedure for making the distinction between ground fog and low-level stratus is proposed based on Moderate Resolution Imaging Spectroradiometer (MODIS, flying on board the NASA Terra and Aqua satellites) daytime data for Germany. Stratus thickness is alternatively derived from either empirical relationships or a newly developed retrieval scheme (lookup table approach), which relies on multiband albedo and radiative transfer calculations. A trispectral visible–near-infrared (VIS–NIR) approach has been proven to give the best results for the calculation of geometrical thickness. The comparison of horizontal visibility data from synoptic observing (SYNOP) stations of the German Weather Service and the results of the ground fog detection schemes reveals that the lookup table approach shows the best performance for both a valley fog situation and an extended layer of low stratus with complex local visibility structures. Even if the results are very encouraging [probability of detection (POD) = 0.76], relatively high percentage errors and false alarm ratios still occur. Uncertainties in the retrieval scheme are mostly due to possible collocation errors and known problems caused by comparing point and pixel data (time lag between satellite overpass and ground observation, etc.). A careful inspection of the pixels that mainly contribute to the false alarm ratio reveals problems with thin cirrus layers and the fog-edge position of the SYNOP stations. Validation results can be improved by removing these suspicious pixels (e.g., percentage error decreases from 28% to 22%).
The Impact of Different WRF Model Physical Parameterizations and Their Interactions on Warm Season MCS Rainfall
Authors: Jankov Isidora , Gallus William A. , Segal Moti , Shaw Brent , Koch Steven E. 
Published: January 2006

Abstract
In recent years, a mixed-physics ensemble approach has been investigated as a method to better predict mesoscale convective system (MCS) rainfall. For both mixed-physics ensemble design and interpretation, knowledge of the general impact of various physical schemes and their interactions on warm season MCS rainfall forecasts would be useful. Adopting the newly emerging Weather Research and Forecasting (WRF) model for this purpose would further emphasize such benefits. To pursue this goal, a matrix of 18 WRF model configurations, created using different physical scheme combinations, was run with 12-km grid spacing for eight International H2O Project (IHOP) MCS cases. For each case, three different treatments of convection, three different microphysical schemes, and two different planetary boundary layer schemes were used. Sensitivity to physics changes was determined using the correspondence ratio and the squared correlation coefficient. The factor separation method was also used to quantify in detail the impacts of the variation of two different physical schemes and their interaction on the simulated rainfall.
Skill score measures averaged over all eight cases for all 18 configurations indicated that no one configuration was obviously best at all times and thresholds. The greatest variability in forecasts was found to come from changes in the choice of convective scheme, although notable impacts also occurred from changes in the microphysics and planetary boundary layer (PBL) schemes. Specifically, changes in convective treatment notably impacted the forecast of system average rain rate, while forecasts of total domain rain volume were influenced by choices of microphysics and convective treatment. The impact of interactions (synergy) of different physical schemes, although occasionally of comparable magnitude to the impacts from changing one scheme alone (compared to a control run), varied greatly among cases and over time, and was typically not statistically significant.
Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a Neural Network Classifier
Authors: Hennon Christopher C. , Marzban Caren , Hobgood Jay S. 
Published: January 2006

AbstractA binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.
Validation of the Coupled NCEP Mesoscale Spectral Model and an Advanced Land Surface Model over the Hawaiian Islands. Part I: Summer Trade Wind Conditions and a Heavy Rainfall Event*
Authors: Zhang Yongxin , Chen Yi-Leng , Hong Song-You , Juang Hann-Ming Henry , Kodama Kevin 
Published: January 2006

Abstract
Validations of the 10-km operational Regional Spectral Model (RSM) and the coupled Mesoscale Spectral Model (MSM) with an advanced land surface model (LSM) forecasts during a 1-month period from 20 May through 20 June 2002 are performed at three surface sites on the island of Oahu. One heavy rainfall case over the Hawaiian Islands is also simulated using the MSM–LSM. Over land with adequate representation of the terrain, the 1.5-km MSM provides better forecasts of surface variables than the 10-km operational RSM. However, there are still appreciable discrepancies between the MSM simulations and observations. Further improvements are achieved by coupling the MSM with the LSM. In particular, overestimation of the surface wind speed and daytime cold biases experienced by the MSM are largely corrected in the coupled MSM–LSM.
Composite analyses of surface variables at three surface sites under different trade wind conditions show that the observed diurnal cycles in 2-m temperature, 2-m dewpoint temperature, and 10-m wind are better forecasted by the MSM–LSM than by the MSM. The observed daytime minima in 2-m dewpoint temperatures during the strong trade wind days at two urban sites are reproduced by the MSM–LSM. The heavy rainfall case studies presented herein indicate that the high-resolution MSM–LSM has better capability in simulating localized rainfall distributions and airflows associated with the heavy rainfall event than the 10-km RSM–LSM. A major model bias is that the MSM–LSM produces excessive rainfall on the windward side of the island of Oahu with no rainfall downstream of the mountain ridges, in contrast to the observed rainfall distribution that shows the maximum rainfall axis occurring slightly downstream of the mountain ridges.
Validation of the Coupled NCEP Mesoscale Spectral Model and an Advanced Land Surface Model over the Hawaiian Islands. Part II: A High Wind Event*
Authors: Zhang Yongxin , Chen Yi-Leng , Kodama Kevin 
Published: January 2006

Abstract
A high wind event (14–15 February 2001) over the Hawaiian Islands associated with a cold front is simulated using the National Centers for Environmental Prediction (NCEP) Mesoscale Spectral Model (MSM) coupled with an advanced land surface model (LSM). During this period, a strong high pressure cell moved to the northeast of the Hawaiian Islands following the passage of the cold front. The cell then merged with the semipermanent subtropical high and resulted in windy conditions across the state of Hawaii. Analyses of soundings from Lihue on Kauai and Hilo on the Big Island reveal a mean-state critical level below 400 hPa, a strong cross-barrier flow (∼13 m s−1), and the presence of a trade wind inversion.
The MSM–LSM predicts downslope windstorms on the lee sides of mountains or ridges with tops beneath the trade wind inversion and within ocean channels between islands. In the case of high mountains with a peak height above the trade wind inversion, weak winds are simulated on the lee side. Around the corners of the islands and in gaps between mountains, gap winds and downslope windstorms are both important for the development of localized leeside windstorms.
The localized windstorms over the Hawaiian Islands develop as a result of interactions between large-scale airflow and the complex local topography. Since the terrain is not adequately resolved by the 10-km RSM–LSM, it is no surprise that these windstorms are better simulated by the high-resolution nonhydrostatic MSM–LSM than the 10-km RSM–LSM.
A Single-Station Approach to Model Output Statistics Temperature Forecast Error Assessment
Authors: Taylor Andrew A. , Leslie Lance M. 
Published: January 2006

Abstract
Error characteristics of model output statistics (MOS) temperature forecasts are calculated for over 200 locations around the continental United States. The forecasts are verified on a station-by-station basis for the year 2001. Error measures used include mean algebraic error (bias), mean absolute error (MAE), relative frequency of occurrence of bias and MAE values, and the daily forecast errors themselves. A case study examining the spatial and temporal evolution of MOS errors is also presented.
The error characteristics presented here, together with the case study, provide a more detailed evaluation of MOS performance than may be obtained from regionally averaged error statistics. Knowledge concerning locations where MOS forecasts have large errors or biases and why those errors or biases exist is of great value to operational forecasters. Not only does such knowledge help improve their forecasts, but forecaster performance is often compared to MOS predictions. Examples of biases in MOS forecast errors are illustrated by examining two stations in detail. Significant warm and cold biases are found in maximum temperature forecasts for Los Angeles, California (LAX), and minimum temperature forecasts for Las Vegas, Nevada (LAS), respectively. MAE values for MOS temperature predictions calculated in this study suggest that coastal stations tend to have lower MAE values and lower variability in their errors, while forecasts with high MAE and error variability are more frequent in the interior of the United States. Therefore, MAE values from samples of MOS forecasts are directly proportional to the variance in the observations. Additionally, it is found that daily maximum temperature forecast errors exhibit less variability during the summer months than they do over the rest of the year, and that forecasts for any one station rarely follow a consistent temporal pattern for more than two or three consecutive days. These inconsistent error patterns indicate that forecasting temperatures based on recent trends in MOS forecast errors at an individual station is usually not a good strategy. As shown in earlier studies by other authors and demonstrated again here, MOS temperature forecasts are often inaccurate in the vicinity of strong temperature gradients, for locations affected by shallow cold air masses, or for stations in regions of anomalously warm or cold temperatures.
Finally, a case study is presented examining the spatial and temporal distributions of MOS temperature forecast errors across the United States from 13 to 15 February 2001. During this period, two surges of cold arctic air moved south into the United States. In contrast to error trends at individual stations, nationwide spatial and temporal patterns of MOS forecast errors could prove to be a powerful forecasting tool. Nationwide plots of errors in MOS forecasts would be useful if made available in real time to operational forecasters.
Performance of Long-Term Integrations of the Japan Meteorological Agency Nonhydrostatic Model Using the Spectral Boundary Coupling Method
Authors: Yasunaga Kazuaki , Sasaki Hidetaka , Wakazuki Yasutaka , Kato Teruyuki , Muroi Chiashi , Hashimoto Akihiro , Kanada Sachie , Kurihara Kazuo , Yoshizaki Masanori , Sato Yasuo 
Published: January 2006

Abstract
The spectral boundary coupling (SBC) method, which is an approach used to couple a limited-area model with a large-scale model, was introduced into a nonhydrostatic model. To investigate whether the SBC method works well in a long-term integration of a high-resolution nonhydrostatic model, two numerical experiments were conducted with a model having a horizontal grid interval of 5 km. In one experiment, the SBC method was employed, while it was not in the other experiment. The time integration in both experiments was over a 40-day period. The nonhydrostatic model was nested into objectively analyzed fields, instead of the forecasts from an extended-area model.
Predicted patterns of sea level pressure and precipitation were compared with objective analyses, and data provided by the Global Precipitation Climatology Project (GPCP), respectively. The predicted rainfall amounts and surface temperature over the Japanese islands were statistically evaluated, making use of the analyzed rainfall and surface data observed by the Japan Meteorological Agency (JMA). All results examined in the present study exhibited better performances with use of the SBC method than those without the SBC method. It was found that the SBC method was highly useful in long-term simulations by a high-resolution nonhydrostatic model.
Performance of National Weather Service Forecasts Compared to Operational, Consensus, and Weighted Model Output Statistics
Authors: Baars Jeffrey A. , Mass Clifford F. 
Published: January 2006

Abstract
Model output statistics (MOS) guidance has been the central model postprocessing approach used by the National Weather Service since the 1970s. A recent advancement in the use of MOS is the application of “consensus” MOS (CMOS), an average of MOS from two or more models. CMOS has shown additional skill over individual MOS forecasts and has performed well compared to humans in forecasting contests. This study compares MOS, CMOS, and WMOS (weighting component MOS predictions by their past performance) forecasts of temperature and precipitation to those of the National Weather Service (NWS) subjective forecasts. Data from 29 locations throughout the United States from 1 August 2003 through 1 August 2004 are used. MOS forecasts from the Global Forecast System (GMOS), Eta (EMOS), and Nested Grid Model (NMOS) models are included, with CMOS being a simple average of these three forecasts. WMOS is calculated using weights determined from a minimum variance method, with varying training periods for each station and variable.
Performance is analyzed at various forecast periods, by region of the United States, and by time/season, as well as for periods of large daily temperature changes or large departures from climatology. The results show that CMOS is competitive or superior to human forecasts at nearly all locations and that WMOS is superior to CMOS. Human forecasts are most skillful compared to MOS during the first forecast day and for periods when temperatures differ greatly from climatology. The implications of these results regarding the future role of human forecasters are examined in the conclusions.
An Odds Ratio Parameterization for ROC Diagram and Skill Score Indices
Authors: Manzato Agostino 
Published: January 2006

Abstract
The relative operating characteristic (ROC) diagram is often used to assess the performance of a classification system, like the categorical forecast of an event occurrence. Categorical forecasting can be obtained by imposing a threshold on a continuous variable in order to make it dichotomous. In practice this threshold could be varied to create different contingency tables. From each table, it is then possible to derive many statistical indices and skill scores, which are functions of the chosen threshold. The ROC curve is obtained by plotting two of these indices: probability of detection (POD) versus probability of false detection (POFD).
In this work a simple approximation for another of these indices, the odds ratio (O), is proposed. Thus, O is parameterized as a function of POFD and that leads to a parameterization of all the theoretical ROC curves. Using this approximation, it is also possible to derive the theoretical maximum Hanssen and Kuipers skill score (KSS) and the theoretical maximum Heidke skill score (HSS), for each ROC. It is found that the maximum HSS depends explicitly on the database event frequency (α), while the KSS seems independent of it.
Out of the approximation framework, some general properties of ROC points corresponding to the maximum KSS, to the maximum HSS, and to the BIAS = 1 condition have also been found. It is also suggested that many of these performance measures are influenced by the event frequency, which must be taken into account when comparing classifiers made for different databases. Another interesting outcome of this study is that it is shown how the KSS is also equitable (in the sense introduced by Gandin and Murphy) for a generic “cost ratio” (λ) between miss and false alarm cases, not only for the original case λ = 1.