Access the full text.
Sign up today, get DeepDyve free for 14 days.
H. Glahn, D. Lowry (1972)
The Use of Model Output Statistics (MOS) in Objective Weather ForecastingJournal of Applied Meteorology, 11
R. Buizza, A. Hollingsworth, F. Lalaurette, A. Ghelli (1999)
Probabilistic Predictions of Precipitation Using the ECMWF Ensemble Prediction SystemWeather and Forecasting, 14
Ashok Kumar, Parvinder Maini, Sheoraj Singh (1999)
An Operational Model for Forecasting Probability of Precipitation and Yes/No ForecastWeather and Forecasting, 14
J. Charba (1998)
The LAMP QPF Products. Part I: Model DevelopmentWeather and Forecasting, 13
David Olson, N. Junker, B. Korty (1995)
Evaluation of 33 Years of Quantitative Precipitation Forecasting at the NMCWeather and Forecasting, 10
J. Michaelsen (1987)
Cross-Validation in Statistical Climate Forecast Models, 26
A. Sigrest, Roman Krzysztofowicz (1998)
Spatially Averaged versus Point Precipitation in Monongahela Basin:Statistical Distinctions for ForecastingWeather and Forecasting, 13
M. Antolik (2000)
An overview of the National Weather Service's centralized statistical quantitative precipitation forecastsJournal of Hydrology, 239
Roman Krzysztofowicz, William Drzal, T. Drake, J. Weyman, Louis Giordano (1993)
Probabilistic Quantitative Precipitation Forecasts for River BasinsWeather and Forecasting, 8
W. Klein, B. Lewis, I. Enger (1959)
OBJECTIVE PREDICTION OF FIVE-DAY MEAN TEMPERATURES DURING WINTERJournal of Meteorology, 16
J. Elsner, C. Schmertmann (1994)
Assessing Forecast Skill through Cross ValidationWeather and Forecasting, 9
D. Rezácová, Václav Motl (1990)
The use of the simple 1D steady-state convective cloud model in the decision tree for determining the probability of thunderstorm occurrenceStudia Geophysica et Geodaetica, 34
H. Bluestein (1992)
Principles of kinematics and dynamics
Tony Hall, H. Brooks, C. Doswell (1999)
Precipitation Forecasting Using a Neural NetworkWeather and Forecasting, 14
R. Kuligowski, A. Barros (1998)
Localized Precipitation Forecasts from a Numerical Weather Prediction Model Using Artificial Neural NetworksWeather and Forecasting, 13
Robert Vislocky, G. Young (1989)
The Use of Perfect Prog Forecasts to Improve Model Output Statistics Forecasts of Precipitation ProbabilityWeather and Forecasting, 4
Statistical interpretation models of the Aire Limitée Adaptation Dynamique Développement International/Limited Area Modelling in Central Europe (ALADIN/LACE) numerical weather prediction (NWP) model outputs have been developed to improve both quantitative and probabilistic precipitation forecasts (QPF and PQPF, respectively) for the warm season. Daily means (from 0600 to 0600 UTC of the next day) of area precipitation are forecast for seven river basins by using the prognostic fields of the NWP model, which began the integration at 0000 UTC. The selected river basins differ in size and mean elevation above mean sea level. A dense network of rain gauges, where the mean distance between the two nearest neighbors is about 8 km, is used to calculate basin average precipitation amounts. Data from three warm seasons (April–September 1998–2000) were used to develop and verify statistical interpretation models. Several statistical models based on multiple linear regression were used to produce QPF and were compared. They estimated either the direct value of the areal mean precipitation or the difference between the NWP model forecast and the actual value. The statistical models also differed in the training data used to develop model parameters. Two different statistical models, multiple linear regression and logistic regression, were used to produce PQPF, and their performances were compared. Model output statistics (MOS) was used to find suitable predictors and to calculate model coefficients. MOS was applied to two seasons; the remaining one served as the independent verification dataset. All three combinations of seasons were considered. The statistical interpretation models significantly improved both the QPF and PQPF of the NWP model forecast. The root-mean-square error of the QPF from the direct NWP model forecast decreased by about 10%–30% for individual river basins.
Weather and Forecasting – American Meteorological Society
Published: Apr 16, 2002
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.