scholarly journals The Impact of Analysis Error on Medium-Range Weather Forecasts

2008 ◽  
Vol 136 (9) ◽  
pp. 3425-3431 ◽  
Author(s):  
Kyle L. Swanson ◽  
Paul J. Roebber
Keyword(s):  
North Pacific Ocean ◽  
Forecast Skill ◽  
Ensemble Spread ◽  
Weather Forecasts ◽  
The North ◽  
Medium Range ◽  
Difference Fields ◽  
Height Fields ◽  
Analysis Error ◽  
The Impact

Abstract All meteorological analyzed fields contain errors, the magnitude of which ultimately determines the point at which a given forecast will fail. Here, the authors explore the extent to which analysis difference fields capture certain aspects of the actual but unknowable flow-dependent analysis error. The analysis difference fields considered here are obtained by subtracting the NCEP and ECMWF reanalysis 500-hPa height fields. It is shown that the magnitude of this 500-hPa analysis difference averaged over the North Pacific Ocean has a statistically significant impact on forecast skill over the continental United States well into the medium range (5 days). Further, it is shown that the impact of this analysis difference on forecast skill is similar to that of ensemble spread well into the medium range, a measure of forecast uncertainty currently used in the operational setting. Finally, the analysis difference and ensemble spread are shown to be independent; hence, the impact of these two quantities upon forecast skill is additive.

Improvement of Medium-Range Forecasts Using the Analog-Dynamical Method

2014 ◽  
Vol 142 (4) ◽  
pp. 1570-1587 ◽  
Author(s):  
Haipeng Yu ◽  
Jianping Huang ◽  
Jifan Chou
Keyword(s):  
Lead Time ◽  
Forecast Skill ◽  
Forecast Errors ◽  
Weather Forecasts ◽  
Planetary Scale ◽  
Anomaly Correlation Coefficient ◽  
Systematic Correction ◽  
Medium Range ◽  
Dynamical Method ◽  
Height Fields

Abstract This study further develops the analog-dynamical method and applies it to medium-range weather forecasts. By regarding the forecast field as a small disturbance superimposed on historical analog fields, historical analog errors can be used to estimate and correct forecast errors. This method is applied to 10-day forecasts from the Global and Regional Assimilation and Prediction System (GRAPES). Both the distribution of atmospheric circulation and the pattern of sea surface temperature (SST) are considered in choosing the analog samples from a historical dataset for 2001–10 based on NCEP Final (FNL) data. The results demonstrate that the analog-dynamical method greatly reduces forecast errors and extends the period of validity of the global 500-hPa height field by 0.8 days, which is superior to results obtained using systematic correction. The correction effect at 500 hPa is increasingly significant when the lead time increases. Although the analogs are selected using 500-hPa height fields, the forecast skill at all vertical levels is improved. The average increase of the anomaly correlation coefficient (ACC) is 0.07, and the root-mean-square error (RMSE) is decreased by 10 gpm on average at a lead time of 10 days. The magnitude of errors for most forecast fields, such as height, temperature, and kinetic energy is decreased considerably by inverse correction. The model improvement is primarily a result of improvement for planetary-scale waves, while the correction for synoptic-scale waves does not affect model forecast skill. As this method is easy to operate and transport to other sophisticated models, it could be appropriate for operational use.

scholarly journals Verification of surface minimum, mean, and maximum temperature forecasts in Calabria for summer 2008

2011 ◽  
Vol 11 (2) ◽  
pp. 487-500 ◽  
Author(s):  
S. Federico
Keyword(s):  
Forecast Error ◽  
Background Field ◽  
Horizontal Resolution ◽  
Atmospheric Modeling ◽  
Forecast Skill ◽  
Maximum Temperature ◽  
Model Forecast ◽  
Maximum Temperatures ◽  
Analysis Error ◽  
The Impact

Abstract. Since 2005, one-hour temperature forecasts for the Calabria region (southern Italy), modelled by the Regional Atmospheric Modeling System (RAMS), have been issued by CRATI/ISAC-CNR (Consortium for Research and Application of Innovative Technologies/Institute for Atmospheric and Climate Sciences of the National Research Council) and are available online at http://meteo.crati.it/previsioni.html (every six hours). Beginning in June 2008, the horizontal resolution was enhanced to 2.5 km. In the present paper, forecast skill and accuracy are evaluated out to four days for the 2008 summer season (from 6 June to 30 September, 112 runs). For this purpose, gridded high horizontal resolution forecasts of minimum, mean, and maximum temperatures are evaluated against gridded analyses at the same horizontal resolution (2.5 km). Gridded analysis is based on Optimal Interpolation (OI) and uses the RAMS first-day temperature forecast as the background field. Observations from 87 thermometers are used in the analysis system. The analysis error is introduced to quantify the effect of using the RAMS first-day forecast as the background field in the OI analyses and to define the forecast error unambiguously, while spatial interpolation (SI) analysis is considered to quantify the statistics' sensitivity to the verifying analysis and to show the quality of the OI analyses for different background fields. Two case studies, the first one with a low (less than the 10th percentile) root mean square error (RMSE) in the OI analysis, the second with the largest RMSE of the whole period in the OI analysis, are discussed to show the forecast performance under two different conditions. Cumulative statistics are used to quantify forecast errors out to four days. Results show that maximum temperature has the largest RMSE, while minimum and mean temperature errors are similar. For the period considered, the OI analysis RMSEs for minimum, mean, and maximum temperatures vary from 1.8, 1.6, and 2.0 °C, respectively, for the first-day forecast, to 2.0, 1.9, and 2.6 °C, respectively, for the fourth-day forecast. Cumulative statistics are computed using both SI and OI analysis as reference. Although SI statistics likely overestimate the forecast error because they ignore the observational error, the study shows that the difference between OI and SI statistics is less than the analysis error. The forecast skill is compared with that of the persistence forecast. The Anomaly Correlation Coefficient (ACC) shows that the model forecast is useful for all days and parameters considered here, and it is able to capture day-to-day weather variability. The model forecast issued for the fourth day is still better than the first-day forecast of a 24-h persistence forecast, at least for mean and maximum temperature. The impact of using the RAMS first-day forecast as the background field in the OI analysis is quantified by comparing statistics computed with OI and SI analyses. Minimum temperature is more sensitive to the change in the analysis dataset as a consequence of its larger representative error.

scholarly journals Tropical and Midlatitude Impact on Seasonal Polar Predictability in the Community Earth System Model

2019 ◽  
Vol 32 (18) ◽  
pp. 5997-6014 ◽  
Author(s):  
Edward Blanchard-Wrigglesworth ◽  
Qinghua Ding
Keyword(s):  
Forecast Skill ◽  
Specific Humidity ◽  
The Arctic ◽  
The North ◽  
Perfect Model ◽  
Community Earth System Model ◽  
Free Running ◽  
Skill Improvement ◽  
The Tropics ◽  
The Impact

Abstract The impact on seasonal polar predictability from improved tropical and midlatitude forecasts is explored using a perfect-model experiment and applying a nudging approach in a GCM. We run three sets of 7-month long forecasts: a standard free-running forecast and two nudged forecasts in which atmospheric winds, temperature, and specific humidity (U, V, T, Q) are nudged toward one of the forecast runs from the free ensemble. The two nudged forecasts apply the nudging over different domains: the tropics (30°S–30°N) and the tropics and midlatitudes (55°S–55°N). We find that the tropics have modest impact on forecast skill in the Arctic or Antarctica both for sea ice and the atmosphere that is mainly confined to the North Pacific and Bellingshausen–Amundsen–Ross Seas, whereas the midlatitudes greatly improve Arctic winter and Antarctic year-round forecast skill. Arctic summer forecast skill from May initialization is not strongly improved in the nudged forecasts relative to the free forecast and is thus mostly a “local” problem. In the atmosphere, forecast skill improvement from midlatitude nudging tends to be largest in the polar stratospheres and decreases toward the surface.

New Methods for Data Storage of Model Output from Ensemble Simulations

2019 ◽  
Vol 147 (2) ◽  
pp. 677-689 ◽  
Author(s):  
Peter D. Düben ◽  
Martin Leutbecher ◽  
Peter Bauer
Keyword(s):  
Data Storage ◽  
Climate Modeling ◽  
Model Resolution ◽  
Ensemble Spread ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Ensemble Simulations ◽  
New Methods ◽  
Weather And Climate ◽  
Medium Range

Abstract Data storage and data processing generate significant cost for weather and climate modeling centers. The volume of data that needs to be stored and data that are disseminated to end users increases with increasing model resolution and the use of larger forecast ensembles. If precision of data is reduced, cost can be reduced accordingly. In this paper, three new methods to allow a reduction in precision with minimal loss of information are suggested and tested. Two of these methods rely on the similarities between ensemble members in ensemble forecasts. Therefore, precision will be high at the beginning of forecasts when ensemble members are more similar, to provide sufficient distinction, and decrease with increasing ensemble spread. To keep precision high for predictable situations and low elsewhere appears to be a useful approach to optimize data storage in weather forecasts. All methods are tested with data of operational weather forecasts of the European Centre for Medium-Range Weather Forecasts.

scholarly journals The Madden–Julian Oscillation and Boreal Winter Forecast Skill: An Analysis of NCEP CFSv2 Reforecasts

2015 ◽  
Vol 28 (15) ◽  
pp. 6297-6307 ◽  
Author(s):  
Charles Jones ◽  
Abheera Hazra ◽  
Leila M. V. Carvalho
Keyword(s):  
Forecast Skill ◽  
Western Pacific ◽  
Boreal Winter ◽  
Lead Times ◽  
Madden Julian Oscillation ◽  
Main Mode ◽  
Weather Forecasts ◽  
Synoptic Variability ◽  
The Impact ◽  
The Western Pacific

Abstract The Madden–Julian oscillation (MJO) is the main mode of tropical intraseasonal variations and bridges weather and climate. Because the MJO has a slow eastward propagation and longer time scale relative to synoptic variability, significant interest exists in exploring the predictability of the MJO and its influence on extended-range weather forecasts (i.e., 2–4-week lead times). This study investigates the impact of the MJO on the forecast skill in Northern Hemisphere extratropics during boreal winter. Several 45-day forecasts of geopotential height (500 hPa) from NCEP Climate Forecast System version 2 (CFSv2) reforecasts are used (1 November–31 March 1999–2010). The variability of the MJO expressed as different amplitudes, durations, and recurrence (i.e., primary and successive events) and their influence on forecast skill is analyzed and compared against inactive periods (i.e., null cases). In general, forecast skill during enhanced MJO convection over the western Pacific is systematically higher than in inactive days. When the enhanced MJO convection is over the Maritime Continent, forecasts are lower than in null cases, suggesting potential model deficiencies in accurately forecasting the eastward propagation of the MJO over that region and the associated extratropical response. In contrast, forecasts are more skillful than null cases when the enhanced convection is over the western Pacific and during long, intense, and successive MJO events. These results underscore the importance of the MJO as a potential source of predictability on 2–4-week lead times.

Towards onset: shades of ENSO skill

2020 ◽  
Author(s):  
Dougal Squire ◽  
James Risbey
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Skill Score ◽  
Forecast Skill ◽  
The North ◽  
Forecast Models ◽  
The Impact ◽  
Better Than

<p>Climate forecast skill for the El Nino-Southern Oscillation (ENSO) is better than chance, but has increased little in recent decades. Further, the relative skill of dynamical and statistical models varies in skill assessments, depending on choices made about how to evaluate the forecasts. Using a suite of models from the North American Multi-Model Ensemble (NMME) archive we outline the consequences for skill of how the bias corrections and forecast anomalies are formed. We show that the method for computing forecast anomalies is such a critical part of the provenance of a skill score that any score for forecast anomalies lacking clarity about the method is open to wide interpretation. Many assessments of hindcast skill are likely to be overestimates of attainable forecast skill because the hindcast anomalies are informed by observations over the period assessed that would not be available to a real forecast. The relative skill rankings of forecast models can change between hindcast and forecast systems because the impact of model bias on skill is sensitive to the ways in which forecast anomalies are formed. Dynamical models are found to be more skillful than simple statistical models for forecasting the onset of El Nino events.</p>

The Impact of Initial Condition and Warm Conveyor Belt Forecast Uncertainty on Variability in the Downstream Waveguide in an ECWMF Case Study

2019 ◽  
Vol 147 (11) ◽  
pp. 4071-4089 ◽  
Author(s):  
Jeremy D. Berman ◽  
Ryan D. Torn
Keyword(s):  
Conveyor Belt ◽  
Forecast Uncertainty ◽  
Ensemble Forecasts ◽  
Rossby Wave Breaking ◽  
Weather Forecasts ◽  
The North ◽  
Weather Events ◽  
High Impact Weather ◽  
Sensitivity Method ◽  
The Impact

Abstract Perturbations to the potential vorticity (PV) waveguide, which can result from latent heat release within the warm conveyor belt (WCB) of midlatitude cyclones, can lead to the downstream radiation of Rossby waves, and in turn high-impact weather events. Previous studies have hypothesized that forecast uncertainty associated with diabatic heating in WCBs can result in large downstream forecast variability; however, these studies have not established a direct connection between the two. This study evaluates the potential impact of latent heating variability in the WCB on subsequent downstream forecasts by applying the ensemble-based sensitivity method to European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of a cyclogenesis event over the North Atlantic. For this case, ensemble members with a more amplified ridge are associated with greater negative PV advection by the irrotational wind, which is associated with stronger lower-tropospheric southerly moisture transport east of the upstream cyclone in the WCB. This transport is sensitive to the pressure trough to the south of the cyclone along the cold front, which in turn is modulated by earlier differences in the motion of the air masses on either side of the front. The position of the cold air behind the front is modulated by upstream tropopause-based PV anomalies, such that a deeper pressure trough is associated with a more progressive flow pattern, originating from Rossby wave breaking over the North Pacific. Overall, these results suggest that more accurate forecasts of upstream PV anomalies and WCBs may reduce forecast uncertainty in the downstream waveguide.

scholarly journals Origin and Impact of Initialization Shocks in Coupled Atmosphere–Ocean Forecasts*

2015 ◽  
Vol 143 (11) ◽  
pp. 4631-4644 ◽  
Author(s):  
David P. Mulholland ◽  
Patrick Laloyaux ◽  
Keith Haines ◽  
Magdalena Alonso Balmaseda
Keyword(s):  
Data Assimilation ◽  
Coupled System ◽  
Atmospheric Temperature ◽  
Ocean Model ◽  
Forecast Skill ◽  
Weather Forecasts ◽  
Model Component ◽  
Medium Range ◽  
Series Of Experiments ◽  
Coupled Data Assimilation

Abstract Current methods for initializing coupled atmosphere–ocean forecasts often rely on the use of separate atmosphere and ocean analyses, the combination of which can leave the coupled system imbalanced at the beginning of the forecast, potentially accelerating the development of errors. Using a series of experiments with the European Centre for Medium-Range Weather Forecasts coupled system, the magnitude and extent of these so-called initialization shocks is quantified, and their impact on forecast skill measured. It is found that forecasts initialized by separate oceanic and atmospheric analyses do exhibit initialization shocks in lower atmospheric temperature, when compared to forecasts initialized using a coupled data assimilation method. These shocks result in as much as a doubling of root-mean-square error on the first day of the forecast in some regions, and in increases that are sustained for the duration of the 10-day forecasts performed here. However, the impacts of this choice of initialization on forecast skill, assessed using independent datasets, were found to be negligible, at least over the limited period studied. Larger initialization shocks are found to follow a change in either the atmosphere or ocean model component between the analysis and forecast phases: changes in the ocean component can lead to sea surface temperature shocks of more than 0.5 K in some equatorial regions during the first day of the forecast. Implications for the development of coupled forecast systems, particularly with respect to coupled data assimilation methods, are discussed.

An ensemble analysis to predict future habitats of striped marlin (Kajikia audax) in the North Pacific Ocean

2013 ◽  
Vol 70 (5) ◽  
pp. 1013-1022 ◽  
Author(s):  
Nan-Jay Su ◽  
Chi-Lu Sun ◽  
André E. Punt ◽  
Su-Zan Yeh ◽  
Gerard DiNardo ◽  
...  
Keyword(s):  
Climate Change ◽  
Pacific Ocean ◽  
Water Temperature ◽  
North Pacific ◽  
North Pacific Ocean ◽  
Additive Models ◽  
The North ◽  
The Impact ◽  
The North Pacific ◽  
Ensemble Analysis

Abstract Su, N.-J., Sun, C.-L., Punt, A. E., Yeh, S.-Z., DiNardo, G., and Chang, Y.-J. 2013. An ensemble analysis to predict future habitats of striped marlin (Kajikia audax) in the North Pacific Ocean. – ICES Journal of Marine Science, 70: 1013–1022. Striped marlin is a highly migratory species distributed throughout the North Pacific Ocean, which shows considerable variation in spatial distribution as a consequence of habitat preference. This species may therefore shift its range in response to future changes in the marine environment driven by climate change. It is important to understand the factors determining the distribution of striped marlin and the influence of climate change on these factors, to develop effective fisheries management policies given the economic importance of the species and the impact of fishing. We examined the spatial patterns and habitat preferences of striped marlin using generalized additive models fitted to data from longline fisheries. Future distributions were predicted using an ensemble analysis, which represents the uncertainty due to several global climate models and greenhouse gas emission scenarios. The increase in water temperature driven by climate change is predicted to lead to a northward displacement of striped marlin in the North Pacific Ocean. Use of a simple predictor of water temperature to describe future distribution, as in several previous studies, may not be robust, which emphasizes that variables other than sea surface temperatures from bioclimatic models are needed to understand future changes in the distribution of large pelagic species.

scholarly journals A Multifaceted Climatology of Atmospheric Blocking and Its Recent Linear Trend

2007 ◽  
Vol 20 (4) ◽  
pp. 633-649 ◽  
Author(s):  
M. Croci-Maspoli ◽  
C. Schwierz ◽  
H. C. Davies
Keyword(s):  
North Pacific ◽  
Linear Trend ◽  
Age Distribution ◽  
Atmospheric Blocking ◽  
Weather Forecasts ◽  
The North ◽  
Four Seasons ◽  
Medium Range ◽  
The Mean ◽  
The North Pacific

Abstract A dynamically based climatology is derived for Northern Hemisphere atmospheric blocking events. Blocks are viewed as large amplitude, long-lasting, and negative potential vorticity (PV) anomalies located beneath the dynamical tropopause. The derived climatology [based on the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40)] provides a concise, coherent, and illuminating description of the main physical characteristics of blocks and the accompanying linear trends. The latitude–longitude distribution of blocking frequency captures the standard bimodal geographical distribution with major peaks over the North Atlantic and eastern North Pacific in all four seasons. The accompanying pattern for the age distribution, the genesis–lysis regions, and the track of blocks reveals that 1) younger blocks (1–4 days) are more prevalent at lower latitudes whereas significantly older blocks (up to 12 days) are located at higher latitudes; 2) genesis is confined predominantly to the two major ocean basins and in a zonal band between 40° and 50°N latitude, whereas lysis is more dispersed but with clear preference to higher latitudes; and 3) the general northeastward–west-northwest movement of blocks in the genesis–lysis phase also exhibits subtle seasonal and intra- and interbasin differences. Examination of the intensity and spatial-scale changes during the blocking life cycle suggests that in the mean a block’s evolution is independent of the genesis region and its eventual duration. A novel analysis of blocking trends reveals significant negative trends in winter over Greenland and in spring over the North Pacific. It is shown that the changes over Greenland are linked to the number of blocking episodes, whereas a neighboring trend signal to the south is linked to higher-frequency anticyclonic systems. Furthermore, evidence is adduced that changes in blocking frequency contribute seminally to tropopause height trends.

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