Fine Mesh Model Research Articles

Tests of a Fine-Mesh Model over Europe and the United States

Abstract Thirty–two 24 h forecasts have been run over western Europe and the eastern United States using a six-layer, 60 km mesh primitive equation model. The forecasts show considerable skill in forecasting cyclogenesis over the Mediterranean and the United States in spite of the inadvertent neglect of surface friction over half of the domain. The average 24 h S 1score for sea level pressure is 39.1 compared to an average of 45.9 for Fleet Numerical Weather Central's operational model and 73.4 for persistence. The initialization scheme is based on an objective analysis of the horizontal wind field. Following the wind analysis, we infer geopotential and temperature from the rotational part of the wind with a nonlinear form of the balance equation. We present detailed results from one initial analysis and error statistics from 30 analyses occurring from December 1977 through April 1978. Typical root-mean-square (rms) differences between first-guess and balanced analyses of geopotential and temperature are ...

A New Operational System for Forecasting Precipitation Type

Abstract A new system is developed which gives conditional probability forecasts for three precipitation type categories: snow or sleet, freezing rain and rain. Also, the probability forecasts are transformed into categorical forecasts so that a “best category”is provided. The Model Output Statistics (MOS) technique is used with output from the Limited-area Fine Mesh (LFM) model to develop statistical forecast equations for each of several regions in the conterminous United States. To help account for the evaporational cooling effect, predictors such as LFM forecasts of boundary layer and 850 mb wet-bulb temperatures and observed surface and dew-point temperatures are included. Also, joint predictors are designed to help account for predictor interactions. The values of the joint predictors are relative frequencies of the freezing rain or snow categories taken from graphs that show these frequencies as joint functions of various pairs of LFM predictors. Results from a statistical screening procedure vary ...

Potential Application of Model Output Statistics (MOS) to Forecasts of Surface Ozone Concentrations

Statistical forecast equations have been developed for the Greater St. Louis, Missouri, area using Model Output Statistics (MOS) derived from the National Meteorological Center's Limited-Area Fine Mesh (LFM) model. They are used to forecast both the probability of ozone concentrations exceeding the 1971 National Ambient Air Quality Standard and the daily 1 h maximum. Predictions extend out to two days (48 h). The application of MOS to forecasts of maximum O3 concentrations lead to skillful [better than chance, persistence or climatology (seasonality)] 24 and 48 h objective predictions. The application of MOS to probability statements about O3 concentrations also resulted in reasonable success for 24 h and 48 h probability forecasts. These forecasts appear sufficiently successful to warrant consideration of the development of other equations for large metropolitan area where O3 concentrations commonly exceed the standard.

Automated Guidance for Predicting Quantitative Precipitation

Abstract An operational, automated guidance system for producing both probability of precipitation amount (PoPA) and categorical forecasts of precipitation amount for 233 cities in the conterminous United States is described. Regionalized equations giving the probabilities of≥0.25, ≥0.50, ≥1.0 and ≥2.0 inches for 6 and 24 h periods for both warm and cool seasons were developed with use of the Model Output Statistics technique. Categorical forecasts are produced by transforming the probability forecasts in such a way as to maximize the threat score. Two sets of guidance products were developed. The first uses predictors from the National Meteorological Center's (NMC) limited Area Fine Mesh (LFM) model, while the second uses predictors from NMC's Primitive Equation (PE) model and the Techniques Development Laboratory's Trajectory model. To test the PoPA system, operational forecasts at 215 cities were verified over a 10-month period. The PoPA categorical forecasts were compared by season to those produced s...

A Model for Predicting Synoptic Weather Types Based on Model Output Statistics

An objective classification model was developed that can automatically predict synoptic weather types in southern California. Stepwise discriminant analysis was used to match the National Meteorological Center's Limited-area Fine Mesh (LFM) Model Output Statistics to subjectively classified weather types or patterns. The five classified weather types range from hot, dry, windy Santa Ana days to cool, rainy, cloudy days caused by a synoptic low. Discriminant function equations were developed for predicting each weather type 12 and 24 h in advance by screening 80 potential predictors consisting of forecasts at 500, 700 and 850 mb from the LFM model. Model output at nine grid points was used because that information adequately describes the meteorological patterns over southern California. Using independent LFM model forecasts valid 12 and 24 h in advance, the objective classification model predicted the probability of the days being in each of the five weather types, then the type with the highest probability was selected. Eighty-eight of 107 24 h periods (days) centered 12 h in advance (81%) were correctly predicted. Of 99 independent days centered 24 h in advance, 71 (72%) were correctly predicted. Hourly means and standard deviations of surface temperature and dew points at eight research sites in the San Bernardino Mountains computed by month for the five weather types had distinct diurnal variations corresponding with weather types. Summarizing hourly temperatures in August at the eight sites by weather type reduced their standard deviation by almost one-half. Measurements of mean daily maximum ozone at a San Bernardino Mountain crest site, where chronic ozone injury to ponderosa pine has occurred, showed significant differences between the weather types. The mean surface wind, temperature and dew-point patterns at 2100 GMT over southern California for type 1 (Santa Ana) days show strong offshore winds, high temperatures and very low dew points, whereas type 5 (synoptic low) days show strong onshore winds, low temperatures and high dew points.

Objective Prediction of Cloud Amount Based on Model Output Statistics

Abstract We have applied the Model Output Statistics (MOS) approach to the prediction of cloudiness. Final guidance warm and cool season forecasting equations were developed by screening forecast fields from the primitive equation and trajectory models. We derived separate equations for each of 233 stations to estimate the probability of clear, scattered, broken and overcast conditions 12 to 48 h in advance. The same predictors were used in all four equations for any given station and projection. In like manner, we also derived a set of early guidance equations for the warm season by screening forecasts from the limited-area fine mesh model. Here, separate equations were developed for 230 stations and projections of 6 to 24 h. Weather parameters from surface reports were also included as potential predictors for the first two forecast projections to provide the latest observed conditions for the early and final guidance systems. We verified both experimental and operational cloud forecasts made from the f...

A Description and Preliminary Results from a Fine-Mesh Model for Forecasting Quantitative Precipitation

Abstract The purpose of this paper is two-fold; first, to document and present a limited-area, moist primitive equation model and, second, to give some preliminary results of experiments testing the sensitivity of the model's quantitative precipitation forecasts to the initial horizontal and vertical relative humidity distribution. Three experiments were performed. The first case used a 1.5 km vertical grid and a Gandin humidity analysis based on standard rawinsonde observations. The results indicate that the model has some skill at forecasting precipitation amounts and location in regions of predominately stable rain and in regions of convective rain. However, some glaring defects in both the initialization of the mass-flow fields and the initialization of the moisture field were evident. The second experiment attempted to enhance the initial moisture field to reflect a narrow band of moisture which was suggested by satellite cloud observations. The inclusion of this moisture band increased the precipita...

Intercomparisons of Data Derived from Nimbus 5 Temperature Profiles, Rawinsonde Observations and Initialized LFM Model Fields

Abstract A 23–24 February 1975 case study is made comparing the results achieved using Nimbus 5 satellite temperature, profiles with those obtained from radiosonde data and the initial hour data of the Limited Area Fine Mesh (LFM) model of the National Meteorological Center. An objective analysis technique is used to construct and analyze isotropic cross sections through an intense baroclinic zone. A cross section based on 1700 GMT Nimbus 5 soundings is compared with those based on 1200 and 0000 GMT radiosonde and LFM data. Geostrophic shear calculations are used to compare the thermal gradients for various isobaric layers obtained from the three data acts. The Nimbus 5 results show somewhat less detail than those based on the radiosonde data, but wore than those obtained from the LPM data. Comparisons of the mean temperature in various isobaric layers for the three time periods indicate that the 1700 GMT Nimbus 5 data appear to be consistent with the changing synoptic pattern. Estimates of the wind compo...

Diagnosing and Predicting Surface Temperature in Mountainous Terrain

Abstract The first two harmonics of a Fourier series temperature expansion were used to model the diurnal variation of surface temperature in mountainous terrain of southern California The temperature T at any hour t was expressed in the formwhere As is the aspect contribution to temperature and is a function of insolation, and Bt, is the bias condition function and depends on the time of day, synoptic weather class and elevation. The Fourier coefficients A0, a1, b1, a2 and b2 are all calculated independently of each other, making it possible to determine the coefficients by regression analysis. Stepwise screening regression was used to derive the Fourier coefficients by means of the “perfect prog” technique. The 17 potential predictors were valid at six times—0, 12 and 24 h in advance from both 0000 and 1200 GMT. The temperature predictions can be updated every 12 h with the input of observed surface and 850 mb data and the Limited-area Fine Mesh (LFM) model output 12 and 24 h predictions. The model then...

A Case Study Comparison of the Performance of Operational Prediction Models Used in the United States

Abstract The performance of the National Weather Service, Air Force and Navy large-scale numerical prediction models was studied for the case of a relatively small-scale but important weather-producing frontal system in the Midwest over the period 1200 GMT 5 October 1974 to 0000 GMT 7 October 1974. Forecasts were analyzed both for the operationally important parameters of precipitation, surface pressure and 500 mb heights and for such key diagnostic parameters as vertical motion and thermal and vorticity advection. Results showed the importance of resolving small synoptic-scale features in the initial conditions as well as the role of model resolution, basic dynamics formulation, and planetary boundary layer representation in the forecasts. There was a wide range of performance among the four models. The National Weather Service (NWS) Limited Fine Mesh Model clearly gave the best 24 h forecasts, compared to all the other models including the NWS Primitive Equation Model.

Testing the Limited Area Fine Mesh Model for Probability of Precipitation Forecasting

Abstract Experimental objective forecasts of probability of precipitation (PoP) were made and verified for a large number of United States cities in order to make preliminary tests of the usefulness of the limited area fine mesh (LFM) model. The Model Output Statistics (MOS) technique was used to derive the forecast equations for the winter and summer seasons. Predictors were selected from forecast output of the Primitive Equation (PE), Trajectory (TJ) and LFM models, and from the sine and cosine of the day of the year. Forecast equations were developed with data from only one winter and one summer season (“small sample equations”) from PE predictors only, TJ predictors only, LFM predictors only, and various groupings of these. The small-sample equations were compared on independent data with each other and with operational equations developed on a much larger data sample. It was found that the small-sample combinations that included the LFM gave better results than the operational equations in winter. Ho...

The adjustment of the wind field to small scale topography in a numerical weather prediction model

Abstract Numerical experiments are performed to test one reasonably economical method of producing regional forecasts. Starting with initial conditions interpolated from a 20 hour coarse grid Northern Hemisphere forecast, a fine mesh model is integrated for a further period of 4 hours over a limited area. The fine mesh is located over the north‐eastern part of North America and its resolution is sufficient to re‐produce topographic features such as the St. Lawrence and Richelieu Valleys. The resulting forecast at hour 24 is then compared with the coarse mesh prediction for the same time. The comparison reveals how the horizontal and vertical components of the wind are affected by the small scale topography. In particular, the channelling effect of the main valleys is demonstrated.

Computationally efficient schemes and boundary conditions for a fine-mesh barotropic model based on the shallow-water equations

Two types of computationally efficient semi-implicit and leap-frog schemes for integrating the shallow-water equations are presented. Their accuracy and stability are investigated, both for the pure initial value problem and for limited-area forecasts. The proper formulation of boundary conditions for the latter case is also discussed. Two sets of stable and sufficiently accurate boundary conditions are given. The practical usefulness of these conditions is also supported by computer experiments. DOI: 10.1111/j.2153-3490.1973.tb01601.x

Experiments with a Regional Fine-Mesh Prediction Model

Abstract Twelve and 24-hr 500-mb forecasts have been made using a hemispheric dependent, limited-area, fine-mesh prediction model. The model is barotropic and makes use of the primitive equations. Boundary conditions are obtained via a coarse-mesh hemispheric model, and are imposed during the subsequent integration of the fine-mesh model. Two methods of initializing the fine-mesh simulation are presented. Comparison between the coarse- and fine-mesh results are shown. The quality of the fine-mesh predictions indicates the validity of the approach.