Data Mining Numerical Model Output for Single-Station Cloud-Ceiling Forecast Algorithms

Abstract 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.

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Numerical Guidance Methods for Decision Support in Aviation Meteorological Forecasting

Weather and Forecasting ◽

10.1175/waf-827.1 ◽

2005 ◽

Vol 20 (1) ◽

pp. 82-100 ◽

Cited By ~ 14

Author(s):

A. J. M. Jacobs ◽

N. Maat

Keyword(s):

Weather Prediction ◽

Forecast Accuracy ◽

Transformation Method ◽

Skill Score ◽

Cloud Base ◽

Model Data ◽

Operational Environment ◽

Automatic Production ◽

Statistical Measures ◽

Nwp Model

Abstract Numerical guidance methods for decision making support of aviation meteorological forecasters are presented. The methods have been developed to enhance the usefulness of numerical weather prediction (NWP) model data and local and upstream observations in the production of terminal aerodrome forecasts (TAFs) and trend-type forecasts (TRENDs) for airports. In this paper two newly developed methods are described and it is shown how they are used to derive numerical guidance products for aviation. The first is a combination of statistical and physical postprocessing of NWP model data and in situ observations. This method is used to derive forecasts for all aviation-related meteorological parameters at the airport. The second is a high-resolution wind transformation method, a technique used to derive local wind at airports from grid-box-averaged NWP model winds. For operational use of the numerical guidance products encoding software is provided for automatic production of an alphanumeric TAF and TREND code. A graphical user interface with an integrated code editor enables the forecaster to modify the suggested automatic codes. For aviation, the most important parameters in the numerical guidance are visibility and cloud-base height. Both have been subjected to a statistical verification analysis, together with their automatically produced codes. The results in terms of skill score are compared to the skill of the forecasters’ TAF and TREND code. The statistical measures suggest that the guidance has the best skill at lead times of +4 h and more. For the short term, mainly trend-type forecasts, the persistence forecast based on recent observations is difficult to beat. Verification has also shown that the wind transformation method, which has been applied to generate 10-m winds at Amsterdam Airport Schiphol, reduces the mean error in the (grid box averaged) NWP model wind significantly. Among the potential benefits of these numerical guidance methods is increasing forecast accuracy. As a result, the related numerical guidance products and encoding software have been integrated in the operational environment for the production of TAFs and TRENDs.

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Remote Cloud Ceiling Assessment Using Data-Mining Methods

Journal of Applied Meteorology ◽

10.1175/jam2177.1 ◽

2004 ◽

Vol 43 (12) ◽

pp. 1929-1946 ◽

Cited By ~ 5

Author(s):

Richard L. Bankert ◽

Michael Hadjimichael ◽

Arunas P. Kuciauskas ◽

William T. Thompson ◽

Kim Richardson

Keyword(s):

Data Mining ◽

Satellite Data ◽

Weather Prediction ◽

Ground Truth ◽

Physical Parameters ◽

Low Cloud ◽

Ceiling Height ◽

Mining Methods ◽

Using Data ◽

Automated Algorithms

Abstract Data-mining methods are applied to numerical weather prediction (NWP) output and satellite data to develop automated algorithms for the diagnosis of cloud ceiling height in regions where no local observations are available at analysis time. A database of hourly records that include Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) output, satellite data, and ground truth observations [aviation routine weather reports (METAR)] has been created. Data were collected over a 2.5-yr period for specific locations in California. Data-mining techniques have been applied to the database to determine relationships in the collected physical parameters that best estimate cloud ceiling conditions, with an emphasis on low ceiling heights. Algorithm development resulted in a three-step approach: 1) determine if a cloud ceiling exists, 2) if a cloud ceiling is determined to exist, determine if the ceiling is high or low (below 1 000 m), and 3) if the cloud ceiling is determined to be low, compute ceiling height. A sample of the performance evaluation indicates an average absolute height error of 120.6 m with a 0.76 correlation and a root-mean-square error of 168.0 m for the low-cloud-ceiling testing set. These results are a significant improvement over the ceiling-height estimations generated by an operational translation algorithm applied to COAMPS output.

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A Comparative Verification of Localized Aviation Model Output Statistics Program (LAMP) and Numerical Weather Prediction (NWP) Model Forecasts of Ceiling Height and Visibility

Weather and Forecasting ◽

10.1175/2010waf2222383.1 ◽

2010 ◽

Vol 25 (4) ◽

pp. 1161-1178 ◽

Cited By ~ 6

Author(s):

David E. Rudack ◽

Judy E. Ghirardelli

Keyword(s):

United States ◽

Short Range ◽

Mesoscale Model ◽

Weather Prediction ◽

Weather Research And Forecasting ◽

Model Output ◽

Probabilistic Forecasts ◽

Ceiling Height ◽

Nwp Model ◽

Model Output Statistics

Abstract In an effort to support aviation forecasting, the National Weather Service’s Meteorological Development Laboratory (MDL) has recently redeveloped the Localized Aviation Model Output Statistics (MOS) Program (LAMP) system. LAMP is designed to run hourly in NWS operations and produce short-range aviation forecast guidance at 1-h projections out to 25 h. This paper compares and contrasts LAMP ceiling height and visibility forecasts with forecasts produced by the 20-km Rapid Update Cycle model (RUC20), the Weather Research and Forecasting Nonhydrostatic Mesoscale Model (WRF-NMM), and the Short-Range Ensemble Forecast system (SREF). RUC20 and WRF-NMM forecasts of continuous ceiling height and visibility were interpolated to stations and converted into categorical forecasts. These interpolated forecasts were also categorized into instrument flight rule (IFR) or lower conditions and verified against LAMP forecasts at stations in the contiguous United States. LAMP and SREF probabilistic forecasts of ceiling height and visibility from LAMP and the SREF system were also verified. This study demonstrates that for the 0000 and 1200 UTC cycles over the contiguous United States, LAMP station-based categorical forecasts of ceiling height, visibility, and IFR conditions or lower are more accurate than the RUC20 and WRF-NMM ceiling height and visibility forecasts interpolated to stations. Moreover, for the 0900 and 2100 UTC forecast cycles and verification periods studied here, LAMP ceiling height and visibility probabilities exhibit better reliability and skill than the SREF system.

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A Comparative Verification of High-Resolution Precipitation Forecasts Using Model Output Statistics

Monthly Weather Review ◽

10.1175/mwr-d-16-0256.1 ◽

2017 ◽

Vol 145 (10) ◽

pp. 4037-4054 ◽

Cited By ~ 5

Author(s):

Emiel van der Plas ◽

Maurice Schmeits ◽

Nicolien Hooijman ◽

Kees Kok

Keyword(s):

High Resolution ◽

Weather Prediction ◽

Skill Score ◽

Model Output ◽

Probability Forecast ◽

Maximum Information ◽

Verification Methods ◽

Skill Scores ◽

Nwp Model ◽

Model Output Statistics

Verification of localized events such as precipitation has become even more challenging with the advent of high-resolution mesoscale numerical weather prediction (NWP). The realism of a forecast suggests that it should compare well against precipitation radar imagery with similar resolution, both spatially and temporally. Spatial verification methods solve some of the representativity issues that point verification gives rise to. In this paper, a verification strategy based on model output statistics (MOS) is applied that aims to address both double-penalty and resolution effects that are inherent to comparisons of NWP models with different resolutions. Using predictors based on spatial precipitation patterns around a set of stations, an extended logistic regression (ELR) equation is deduced, leading to a probability forecast distribution of precipitation for each NWP model, analysis, and lead time. The ELR equations are derived for predictands based on areal-calibrated radar precipitation and SYNOP observations. The aim is to extract maximum information from a series of precipitation forecasts, like a trained forecaster would. The method is applied to the nonhydrostatic model Harmonie-AROME (2.5-km resolution), HIRLAM (11-km resolution), and the ECMWF model (16-km resolution), overall yielding similar Brier skill scores for the three postprocessed models, but somewhat larger differences for individual lead times. In addition, the fractions skill score is computed using the three deterministic forecasts, showing slightly higher skill for the Harmonie-AROME model. In other words, despite the realism of Harmonie-AROME precipitation forecasts, they only perform similarly or somewhat better than precipitation forecasts from the two lower-resolution models, at least in the Netherlands.

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The Use of Gridded Model Output Statistics (GMOS) in Energy Forecasting of a Solar Car

Energies ◽

10.3390/en13081984 ◽

2020 ◽

Vol 13 (8) ◽

pp. 1984

Author(s):

Christiaan Oosthuizen ◽

Barend Van Wyk ◽

Yskandar Hamam ◽

Dawood Desai ◽

Yasser Alayli

Keyword(s):

Weather Forecasting ◽

Forecast Error ◽

Weather Prediction ◽

Weather Conditions ◽

Model Output ◽

Energy Forecasting ◽

Weather Forecasts ◽

Improved Performance ◽

Model Output Statistics ◽

Auxiliary Services

For many years, primary weather forecasting services (Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF)) have been made available to the public through global Numerical Weather Prediction (NWP) models estimating a multitude of general weather variables in a variety of resolutions. Secondary services such as weather experts Meteomatics AG use data and improve the forecasts through various methods. They tailor for the specific needs of customers in the wind and solar power generation sector as well as data scientists, analysts, and meteorologists in all areas of business. These auxiliary services have improved performance and provide reliable data. However, this work extended these auxiliary services to so-called tertiary services in which the weather forecasts were further conditioned for the very niche application environment of mobile solar technology in solar car energy management. The Gridded Model Output Statistics (GMOS) Global Horizontal Irradiance (GHI) model developed in this work utilizes historical data from various ground station locations in South Africa to reduce the mean forecast error of the GHI component. An average Root Mean Square Error (RMSE) improvement of 11.28% was shown across all locations and weather conditions. It was also shown how the incorporation of the GMOS model could have increased the accuracy in regard to the State of Charge (SoC) energy simulation of a solar car during the Sasol Solar Challenge 2018 and the possible range benefits thereof.

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Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications

Solar Energy ◽

10.1016/j.solener.2015.06.005 ◽

2015 ◽

Vol 118 ◽

pp. 634-645 ◽

Cited By ~ 22

Author(s):

Remco A. Verzijlbergh ◽

Petra W. Heijnen ◽

Stephan R. de Roode ◽

Alexander Los ◽

Harm J.J. Jonker

Keyword(s):

Numerical Weather Prediction ◽

Solar Power ◽

Weather Prediction ◽

Model Output ◽

Numerical Weather ◽

Model Output Statistics ◽

Improved Model

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The LQ-WARN Project – Development of a Model Output Statistics Product for Air Quality Warnings

10.5194/egusphere-egu21-882 ◽

2021 ◽

Author(s):

Sabine Robrecht ◽

Andreas Lambert ◽

Stefan Gilge

Keyword(s):

Air Quality ◽

Meteorological Parameters ◽

Phase 1 ◽

Weather Prediction ◽

Model Output ◽

Operational Mode ◽

Quality Models ◽

Air Quality Forecast ◽

Direct Model ◽

Model Output Statistics

In order to reach legal air quality limits, several municipalities in Germany have decided to take actions if concentrations of NO2 and Particulate Matter (PM) exceed certain thresholds. The decision for concrete measures is usually based on observations or use the Direct Model Output (DMO) of air quality models. However, due to large biases of state-of-the-art numerical air quality models, the skill of DMO forecasts to predict periods of polluted air up to four days ahead is very limited.The project LQ-WARN aims to develop a system for warning of poor air quality based on Model Output Statistics (MOS). Therefore, air quality related observations, model results provided by the Copernicus Atmosphere Monitoring Service (CAMS) and meteorological parameters from the ECMWF numerical weather prediction model are used as predictors to forecast the air quality by applying Multiple Linear Regression (MLR). In this way MOS equations are calculated for four seasons. The final forecast product will comprise post-processed probabilistic as well as deterministic (e.g. mass concentration) parameters for the species NO2, O3, PM10 and PM2.5. Forecasts will be available for several hundred German locations and cover lead times up to 96 hours.Here, we show first results of our phase 1 MOS prototype, for which observational, meteorological and empirical predictors are applied. Despite of the preliminary exclusion of CAMS predictors, the verifications of the MOS equations imply a considerable reduction of variance and a significant reduction of RMSE (Root Mean Square Error) compared to the climatological values for all four species. Hence, the MOS system can already provide a reasonably good air quality forecast. Furthermore, our analysis of used meteorological predictors, enables a detailed analysis of the importance of specific meteorological parameters for improved statistical air quality forecasts. &#160;As an outlook we will provide detailed information about the final phase 2 LQ-WARN product, which will also include the MOS predictors of CAMS and is expected to be launched in pre-operational mode by 2022.

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A New Process for Producing First Guess TAFs

10.5194/egusphere-egu2020-5154 ◽

2020 ◽

Author(s):

Andre Lanyon ◽

Jessica Standen ◽

Piers Buchanan

Keyword(s):

Weather Prediction ◽

Future Research ◽

Forecast Performance ◽

New Process ◽

Low Cloud ◽

Production Research ◽

Nwp Model ◽

The Uk ◽

Consultation Services ◽

Further Development

Terminal Aerodrome Forecasts (TAFs) are a widely accepted international form of aviation forecast used for flight planning procedures at all major airports. TAF production in the UK is currently a time-consuming, manual process carried out by Operational Meteorologists. It has long been speculated that providing a numerical weather prediction (NWP) model-derived first guess solution could bring large improvements in the efficiency of TAF production. Research into first guess TAFs has a long history but making progress has been challenging. However, significant progress has been made at the Met Office in recent months. A practical approach has been adopted that draws on experience of manually producing TAFs. Although NWP model data is utilised as much as possible, steps have been taken to ensure the first guess TAFs are kept as simple and readable as possible whilst retaining information important to the customer. By taking this approach, it is hoped that the first guess TAFs will require minimal intervention from Operational Meteorologists in the majority of weather situations. Development of first guess TAFs is still in the preliminary stages and not all weather parameters are currently included. However, they are produced in such a way that they can be verified using standard Met Office methods, allowing objective comparison with operationally issued TAFs. Verification scores analysed over a 3 year period are encouraging and suggest that forecast performance of first guess TAFs is generally similar to that of operationally issued TAFs. Occasionally, some large differences become apparent when comparing forecasts of rare events such as mist, fog and very low cloud bases, and this is likely to be an area of future research. With further development, it is speculated that the use of first guess TAFs could significantly reduce TAF production time, allowing Operational Meteorologists to make better use of their expertise, perhaps by adding value to model output or by providing valuable consultation services to aviation customers.

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The Use of Structure Functions and Spectra from Numerical Model Output to Determine Effective Model Resolution

Monthly Weather Review ◽

10.1175/2007mwr2250.1 ◽

2008 ◽

Vol 136 (4) ◽

pp. 1537-1553 ◽

Cited By ~ 45

Author(s):

Rod Frehlich ◽

Robert Sharman

Keyword(s):

Spatial Structure ◽

Spatial Statistics ◽

Weather Prediction ◽

Structure Functions ◽

Effective Model ◽

Model Output ◽

Model Resolution ◽

Nwp Model ◽

Spatial Spectra ◽

Aircraft Data

Abstract The effective model resolution of three numerical weather prediction (NWP) models is determined from analyses of spatial structure functions and spatial spectra. In this paper, the effective resolution is defined as the dimensions of the rectangular spatial filter that describes the net effect of all of the NWP model’s numerical filtering and smoothing effects. These effects are determined by comparison of spatial statistics of the NWP model output with statistical climatologies derived from aircraft data for the upper troposphere and lower stratosphere. The comparisons are based on both spatial structure functions and spatial spectra. The structure function approach has fewer assumptions and fewer numerical artifacts. Accurate estimates of NWP effective model resolution require a robust climatology of the spatial statistics, which are a function of latitude and location, such as over mountainous regions. An artifact in the climatology of the velocity statistics resulting from mountain waves is identified from NWP model output and corroborated with research aircraft data, which has not been previously observed in global statistical climatologies.

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Atmospheric Motion Vectors from Model Simulations. Part II: Interpretation as Spatial and Vertical Averages of Wind and Role of Clouds

Journal of Applied Meteorology and Climatology ◽

10.1175/jamc-d-12-0337.1 ◽

2014 ◽

Vol 53 (1) ◽

pp. 65-82 ◽

Cited By ~ 11

Author(s):

Angeles Hernandez-Carrascal ◽

Niels Bormann

Keyword(s):

Weather Prediction ◽

Cloud Layer ◽

Cloud Base ◽

Random Errors ◽

Motion Vectors ◽

Single Level ◽

Model Cloud ◽

Atmospheric Motion ◽

Nwp Model ◽

Atmospheric Motion Vectors

AbstractThis is the second part of a two-part paper whose main objective is to improve the characterization of atmospheric motion vectors (AMVs) and their errors to guide developments in the use of AMVs in numerical weather prediction (NWP). AMVs tend to exhibit considerable systematic and random errors. These errors can arise in the AMV derivation or the interpretation of AMVs as single-level point estimates of wind. An important difficulty in the study of AMV errors is the scarcity of collocated observations of clouds and wind. The study uses instead a simulation framework: geostationary imagery for Meteorological Satellite-8 (Meteosat-8) is generated from a high-resolution simulation with the Weather Research and Forecasting regional model, and AMVs are derived from sequences of these simulated images. The NWP model provides the “truth” with a sophisticated description of the atmosphere. This second part focuses on alternative interpretations of AMVs. The key results are 1) that interpreting the AMVs as vertical and horizontal averages of wind can give some benefits over the traditional single-level interpretation (improvements in RMSVD of 5% for high-level AMVs and 20% for low-level AMVs) and 2) that there is evidence that AMVs are more representative of either a wind average over the model cloud layer or wind at a representative level within the cloud layer than of wind at the model cloud top or cloud base.

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