Some aspects of the improvement in skill of numerical weather prediction

By A. Simmons, A. Hollingsworth. 2002

547 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/cfacab9a5fd4668d52fa31bcd56fe096e7411a96

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Feasibility of a 100-Year Reanalysis Using Only Surface Pressure Data (2006, background): “We will refer to this forecast error as “modern NWP forecast error,” because it is representative of operational NWP forecast errors over the period (Simmons and Hollingsworth 2002).1 All three systems are capable of making 6-hourly height analyses that are “useful” as defined by errors smaller…”
Accounting for the Error due to Unresolved Scales in Ensemble Data Assimilation: A Comparison of Different Approaches (2005, background): “Model error is a very prominent component of the total error in these experiments, arguably more than it is in operational ensemble forecasting (e.g., Simmons and Hollingsworth 2002, their Fig.”
The potential of the ensemble Kalman filter for NWP—a comparison with 4D‐Var (2003, methodology): “This is important since model forecasts used in data assimilation are now typically as accurate as radiosonde observations (Simmons and Hollingsworth 2002), while satellite soundings have error characteristics which can be more easily represented in the direct variational assimilation of radiances…”
An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test (2008, unknown): “The 4DVAR method can gain the optimal trajectory as a nonsequen-tial assimilation algorithm, which can effectively assimilate high temporal–spatial resolution data, for example, satellite observations (Xiao et al. 2002; Simmons and Hollingsworth 2002).”
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments (2006, background): “Despite this, model error, especially that at the mesoscale, is generally hard to identify and to deal with due to the chaotic nature of the atmosphere, its flow-dependent characteristics, and the lack of sufficiently dense observations for verification (e.g., Orrell et al. 2001; Orrell 2002; Simmon”
Significance of changes in medium-range forecast scores (2016, methodology): “Although a significance test of paired differences is routinely used at forecasting centres, and the fundamentals are covered by Wilks (2006), there is little else in the literature on forecast verification.”
A signal-to-noise paradox in climate science (2018, background): “Estimates of the time horizon for predictability of individual weather events is typically 2 weeks for the mid-latitude atmosphere.(11,12) However, some components of the climate system are predictable well beyond these timescales.”
Effects of stochastic parametrizations in the Lorenz '96 system (2005, methodology): “This paper has explored the use of stochastic parametrizations in the Lorenz (1996) model.”
Average Predictability Time. Part II: Seamless Diagnoses of Predictability on Multiple Time Scales (2009, unknown): “That is, if one computes predictability, based on instantaneous states, predictability decays until essentially all predictability is lost after 2 weeks (Simmons and Hollingsworth 2002).”
Advances in simulating atmospheric variability with the ECMWF model: From synoptic to decadal time‐scales (2008, background): “To put these results into perspective we refer to Simmons and Hollingsworth (2002) who reported that during the last decade the improvement in predictability in sea-level pressure and 500 hPa geopotential height amounts to about 1-day in the Northern Hemisphere and about 2-days in the Southern…”
The forecast skill horizon (2015, background): “Later on, Simmons and Hollingsworth (2002) also revisited the error growth model of Lorenz (1982) and estimated error doubling times as low as 1.5 days for 500 hPa geopotential in the Northern Hemisphere extratropics in boreal winter.”
Ensemble Data Assimilation with the NCEP Global Forecast System (2008, background): “We have verified that this is indeed the case3, especially in the Southern Hemisphere where satellite radiances have been found to have the largest impact on analysis quality (e.g. Derber and Wu 1998; Simmons and Hollingsworth 2002).”
SAL—A Novel Quality Measure for the Verification of Quantitative Precipitation Forecasts (2008, methodology): “Quality measures like the rootmean-square (RMS) difference or anomaly correlations are simple in terms of implementation and are therefore routinely used to monitor and compare general forecast quality at operational prediction centers (e.g., Simmons and Hollingsworth 2002).”
Ensemble forecasting (2008, background): “Some of the recent changes of the assimilation and forecasting system which have lead to improvements in forecast skill over the past years are described in [36].”
ECMWF Analyses and Forecasts of Stratospheric Winter Polar Vortex Breakup: September 2002 in the Southern Hemisphere and Related Events (2005, background): “Moreover, medium-range forecasts for the Southern Hemisphere troposphere have been brought to levels of accuracy similar to those reached for the Northern Hemisphere troposphere (Simmons and Hollingsworth 2002).”
Real-Time Mesoscale Modeling Over Antarctica: The Antarctic Mesoscale Prediction System* (2003, background): “…its advanced data assimilation system, including 4DVAR (four-dimensional variational data assimilation) and use of satellite infrared and microwave radiances from polar orbiters—improvements that benefit the data-sparse high latitudes (see Simmons and Hollingsworth 2002; Mahfouf and Rabier 2000).”