Short-Range Ensemble Forecast Post-processing

Abstract A short-range ensemble forecast system was constructed over the northeast United States down to 12-km grid spacing using 18 members from the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). The ensemble consisted of 12 physics members with varying planetary boundary layer schemes and convective parameterizations as well as seven different initial conditions (ICs) [five National Centers for Environmental Prediction (NCEP) Eta-bred members at 2100 UTC and the 0000 UTC NCEP Global Forecast System (GFS) and Eta runs]. The full 18-member ensemble (ALL) was verified at the surface for the warm (May–September 2003) and cool (October 2003–March 2004) seasons. A randomly chosen subset of seven physics (PHS) members at each forecast hour was used to quantitatively compare with the seven IC members. During the warm season, the PHS ensemble predictions for surface temperature and wind speed had more skill than the IC ensemble and a control (shared PHS and IC member) run initialized 12 h later (CTL12). During the cool and warm seasons, a 14-day running-mean bias calibration applied to the ALL ensemble (ALLBC) added 10%–30% more skill for temperature, wind speed, and sea level pressure, with the ALLBC far outperforming the CTL12. For the 24-h precipitation, the PHS ensemble had comparable probabilistic skill to the IC ensemble during the warm season, while the IC subensemble was more skillful during the cool season. All ensemble members had large diurnal surface biases, with ensemble variance approximating ensemble uncertainty only for wind direction. Selection of ICs was also important, because during the cool season the NCEP-bred members introduced large errors into the IC ensemble for sea level pressure, while none of the subensembles (PHS, IC, or ALL) outperformed the GFS–MM5 for sea level pressure.

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Ensemble forecast post-processing over Belgium: comparison of deterministic-like and ensemble regression methods

Meteorological Applications ◽

10.1002/met.217 ◽

2011 ◽

Vol 18 (1) ◽

pp. 94-104 ◽

Cited By ~ 12

Author(s):

Stéphane Vannitsem ◽

Renate Hagedorn

Keyword(s):

Ensemble Forecast ◽

Post Processing ◽

Regression Methods ◽

Ensemble Regression

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Seasonal Ensemble Forecast Post-processing

Handbook of Hydrometeorological Ensemble Forecasting ◽

10.1007/978-3-642-40457-3_37-2 ◽

2018 ◽

pp. 1-27

Author(s):

Andy Wood ◽

A. Sankarasubramanian ◽

Pablo Mendoza

Keyword(s):

Ensemble Forecast ◽

Post Processing

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Short-Range Ensemble Forecasts of Precipitation Type

Weather and Forecasting ◽

10.1175/waf871.1 ◽

2005 ◽

Vol 20 (4) ◽

pp. 609-626 ◽

Cited By ~ 19

Author(s):

Matthew S. Wandishin ◽

Michael E. Baldwin ◽

Steven L. Mullen ◽

John V. Cortinas

Keyword(s):

Short Range ◽

Ensemble Forecast ◽

Ensemble Forecasting ◽

Precipitation Forecast ◽

Initial Condition ◽

Forecast System ◽

Ensemble Forecasts ◽

Probabilistic Forecasts ◽

Precipitation Type ◽

Forecast Quality

Abstract Short-range ensemble forecasting is extended to a critical winter weather problem: forecasting precipitation type. Forecast soundings from the operational NCEP Short-Range Ensemble Forecast system are combined with five precipitation-type algorithms to produce probabilistic forecasts from January through March 2002. Thus the ensemble combines model diversity, initial condition diversity, and postprocessing algorithm diversity. All verification numbers are conditioned on both the ensemble and observations recording some form of precipitation. This separates the forecast of type from the yes–no precipitation forecast. The ensemble is very skillful in forecasting rain and snow but it is only moderately skillful for freezing rain and unskillful for ice pellets. However, even for the unskillful forecasts the ensemble shows some ability to discriminate between the different precipitation types and thus provides some positive value to forecast users. Algorithm diversity is shown to be as important as initial condition diversity in terms of forecast quality, although neither has as big an impact as model diversity. The algorithms have their individual strengths and weaknesses, but no algorithm is clearly better or worse than the others overall.

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Regime-dependent statistical post-processing of ensemble forecasts

10.5194/egusphere-egu2020-22422 ◽

2020 ◽

Author(s):

Sam Allen ◽

Christopher Ferro ◽

Frank Kwasniok

Keyword(s):

Initial Conditions ◽

Weather Prediction ◽

Systematic Errors ◽

Ensemble Forecast ◽

State Variables ◽

Post Processing ◽

Ensemble Forecasts ◽

Nwp Model ◽

Processing Techniques ◽

Geographical Locations

<p>A number of realizations of one or more numerical weather prediction (NWP) models, initialised at a variety of initial conditions, compose an ensemble forecast. These forecasts exhibit systematic errors and biases that can be corrected by statistical post-processing. Post-processing yields calibrated forecasts by analysing the statistical relationship between historical forecasts and their corresponding observations. This article aims to extend post processing methodology to incorporate atmospheric circulation. The circulation, or flow, is largely responsible for the weather that we experience and it is hypothesized here that relationships between the NWP model and the atmosphere depend upon the prevailing flow. Numerous studies have focussed on the tendency of this flow to reduce to a set of recognisable arrangements, known as regimes, which recur and persist at fixed geographical locations. This dynamical phenomenon allows the circulation to be categorized into a small number of regime states. In a highly idealized model of the atmosphere, the Lorenz &#8216;96 system, ensemble forecasts are subjected to well-known post-processing techniques conditional on the system's underlying regime. Two different variables, one of the state variables and one related to the energy of the system, are forecasted and considerable improvements in forecast skill upon standard post-processing are seen when the distribution of the predictand varies depending on the regime. Advantages of this approach and its inherent challenges are discussed, along with potential extensions for operational forecasters.</p>

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Application of post-processing tools to improve visualisation and quality of numerical short-range predictions over Central Europe

Meteorological Applications ◽

10.1017/s1350482797000509 ◽

1997 ◽

Vol 4 (3) ◽

pp. 219-228 ◽

Cited By ~ 1

Author(s):

Gerald Spreitzhofer

Keyword(s):

Central Europe ◽

Short Range ◽

Post Processing

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Verification of Precipitation Forecasts from NCEP’s Short-Range Ensemble Forecast (SREF) System with Reference to Ensemble Streamflow Prediction Using Lumped Hydrologic Models

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-036.1 ◽

2012 ◽

Vol 13 (3) ◽

pp. 808-836 ◽

Cited By ~ 16

Author(s):

James D. Brown ◽

Dong-Jun Seo ◽

Jun Du

Keyword(s):

Sierra Nevada ◽

Short Range ◽

Ensemble Forecast ◽

Type I ◽

Type Ii ◽

Streamflow Prediction ◽

Southern Plains ◽

Middle Atlantic ◽

Forecast Lead Time ◽

High Precipitation

Abstract Precipitation forecasts from the Short-Range Ensemble Forecast (SREF) system of the National Centers for Environmental Prediction (NCEP) are verified for the period April 2006–August 2010. Verification is conducted for 10–20 hydrologic basins in each of the following: the middle Atlantic, the southern plains, the windward slopes of the Sierra Nevada, and the foothills of the Cascade Range in the Pacific Northwest. Mean areal precipitation is verified conditionally upon forecast lead time, amount of precipitation, season, forecast valid time, and accumulation period. The stationary block bootstrap is used to quantify the sampling uncertainties of the verification metrics. In general, the forecasts are more skillful for moderate precipitation amounts than either light or heavy precipitation. This originates from a threshold-dependent conditional bias in the ensemble mean forecast. Specifically, the forecasts overestimate low observed precipitation and underestimate high precipitation (a type-II conditional bias). Also, the forecast probabilities are generally overconfident (a type-I conditional bias), except for basins in the southern plains, where forecasts of moderate to high precipitation are reliable. Depending on location, different types of bias correction may be needed. Overall, the northwest basins show the greatest potential for statistical postprocessing, particularly during the cool season, when the type-I conditional bias and correlations are both high. The basins of the middle Atlantic and southern plains show less potential for statistical postprocessing, as the type-II conditional bias is larger and the correlations are weaker. In the Sierra Nevada, the greatest benefits of statistical postprocessing should be expected for light precipitation, specifically during the warm season, when the type-I conditional bias is large and the correlations are strong.

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