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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. ...
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. Systematic assessment shows that this annual prediction system has higher predictability for summer climate in tropic than in extra-tropic area, and higher predictabilities over ocean than over land for the fields of precipitation, sea level pressure and surface air temperature; for 500 hPa geopotential height field, the predictability assuming a zonal distribution decreases from tropic to middle-high latitudes, and in China it is the highest among those of all fields. Correlation analysis shows that the prediction ability of IAP annual prediction system to summer temperature is higher than that to precipitation, and the prediction skill can be remarkably improved by the correction system. Furthermore, the comparison between annual and extraseasonal hindcasts indicates that precipitation hindcasted extraseasonally is better than that done annually, and the major discrepancy exists in middle-high latitudes.
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摘要 :
The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. ...
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The IAP numerical annual climate prediction system has been presented in this paper. In order to evaluate this annual prediction system, annual ensemble hindcast experiments over a 21-year period from 1980 to 2000 have been done. Systematic assessment shows that this annual prediction system has higher predictability for summer climate in tropic than in extra-tropic area, and higher predictabilities over ocean than over land for the fields of precipitation, sea level pressure and surface air temperature; for 500 hPa geopotential height field, the predictability assuming a zonal distribution decreases from tropic to middle-high latitudes, and in China it is the highest among those of all fields. Correlation analysis shows that the prediction ability of IAP annual prediction system to summer temperature is higher than that to precipitation, and the prediction skill can be remarkably improved by the correction system. Furthermore, the comparison between annual and extraseasonal hindcasts indicates that precipitation hindcasted extraseasonally is better than that done annually, and the major discrepancy exists in middle-high latitudes.
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Tropical cyclone tracks in the Australian basin are predicted by an analog ensemble forecast model. It is self-adapting in its search of optimal ensemble members from historic cyclone tracks by creating a metric that minimizes the...
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Tropical cyclone tracks in the Australian basin are predicted by an analog ensemble forecast model. It is self-adapting in its search of optimal ensemble members from historic cyclone tracks by creating a metric that minimizes the error of the ensemble mean forecast. When compared with the climatology-persistence reference model, the adapted analog forecasts achieve great-circle errors that improve the reference model by 15%-20%. Ensemble mean forecast errors grow almost linearly with ensemble spread. [References: 20]
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One of the possible ways of evaluation of the earthquake prediction efficiency is a posterior count of successful predictions compared with the number of all issued predictions and the number of all events which should be predicte...
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One of the possible ways of evaluation of the earthquake prediction efficiency is a posterior count of successful predictions compared with the number of all issued predictions and the number of all events which should be predicted. Two efficiency parameters are in general use: the success rate defined as a percentage of the successful predictions in the total number of issued predictions and the alarm rate defined as a percentage of the successful predictions in the total number of events which should be predicted.
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Genomic prediction is widely used to select candidates
for breeding. Size and composition of the reference
population are important factors influencing prediction
accuracy. In Holstein dairy cattle, large reference
populations...
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Genomic prediction is widely used to select candidates
for breeding. Size and composition of the reference
population are important factors influencing prediction
accuracy. In Holstein dairy cattle, large reference
populations are used, but this is difficult to achieve in
numerically small breeds and for traits that are not
routinely recorded. The prediction accuracy is usually
estimated using cross-validation, requiring the full data
set. It would be useful to have a method to predict the
benefit of multibreed reference populations that does
not require the availability of the full data set. Our
objective was to study the effect of the size and breed
composition of the reference population on the accuracy
of genomic prediction using genomic BLUP and
Bayes R. We also examined the effect of trait heritability
and validation breed on prediction accuracy. Using
these empirical results, we investigated the use of a
formula to predict the effect of the size and composition
of the reference population on the accuracy of genomic
prediction. Phenotypes were simulated in a data set
containing real genotypes of imputed sequence variants
for 22,752 dairy bulls and cows, including Holstein, Jersey,
Red Holstein, and Australian Red cattle. Different
reference populations were constructed, varying in size
and composition, to study within-breed, multibreed,
and across-breed prediction. Phenotypes were simulated
varying in heritability, number of chromosomes,
and number of quantitative trait loci. Genomic prediction
was carried out using genomic BLUP and Bayes R.
We used either the genomic relationship matrix (GRM)
to estimate the number of independent chromosomal
segments and subsequently to predict accuracy, or the
accuracies obtained from single-breed reference populations
to predict the accuracies of larger or multibreed
reference populations. Using the GRM overestimated
the accuracy; this overestimation was likely due to close
relationships among some of the reference animals.
Consequently, the GRM could not be used to predict
the accuracy of genomic prediction reliably. However,
a method using the prediction accuracies obtained by
cross-validation using a small, single-breed reference
population predicted the accuracy using a multibreed
reference population well and slightly overestimated
the accuracy for a larger reference population of the
same breed, but gave a reasonably close estimate of the
accuracy for a multibreed reference population. This
method could be useful for making decisions regarding
the size and composition of the reference population.
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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosp...
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Skill for initialized decadal predictions for atmospheric and terrestrial variability is posited to reside in successful prediction of sea surface temperatures (SSTs) associated with the low-frequency modes of coupled ocean-atmosphere variability, for example, Pacific Decadal Oscillation (PDO) or Atlantic Multi-decadal Oscillation (AMO). So far, assessments of the skill of atmospheric and terrestrial variability in decadal predictions, however, have not been encouraging. Similarly, in the context of seasonal climate variability, teleconnections between SSTs associated with PDO and AMO and terrestrial climate have also been noted, but the same SST information used in predictive mode has failed to demonstrate convincing gains in skill. Are these results an artifact of model biases, or more a consequence of some fundamental property of coupled evolution of ocean-atmosphere system in extratropical latitudes, and the manner in which extratropical SST anomalies modulate (or constrain) atmospheric variability? Based on revisiting an analysis of a simple model that replicates the essential characteristics of coupled ocean-atmosphere interaction in extratropical latitudes, it is demonstrated that lack of additional skill in predicting atmospheric and terrestrial variability is more a consequence of fundamental characteristics of coupled evolution of ocean-atmosphere system. The results based on simple models are also substantiated following an analysis of a set of seasonal hind-casts with a fully coupled model.
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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study ...
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The predictability of data values is studied at a fundamental level. Two basic predictor models are defined: Computational predictors perform an operation on previous values to yield predicted next value values. Examples we study are stride value prediction and last value prediction; Contest-Based predictors match recent value history (context) with previous value history and predict values based entirely on previously observed patterns. To understand the potential of value prediction we perform simulations with unbounded prediction tables that are immediately updated using correct data values.
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In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier predictio...
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In this study, the potential predictability of the Northern America (NA) surface air temperature (SAT) was explored using an information-based predictability framework and two multiple model ensemble products: a one-tier prediction by coupled models (T1), and a two-tier prediction by atmospheric models only (T2). Furthermore, the potential predictability was optimally decomposed into different modes for both T1 and T2, by extracting the most predictable structures. Emphasis was placed on the comparison of the predictability between T1 and T2. It was found that the potential predictability of the NA SAT is seasonal and spatially dependent in both T1 and T2. Higher predictability occurs in spring and winter and over the southeastern US and northwestern Canada. There is no significant difference of potential predictability between T1 and T2 for most areas of NA, although T1 has higher potential predictability than T2 in the southeastern US. Both T1 and T2 display similar most predictable components (PrCs) for the NA SAT, characterized by the inter-annual variability mod and the long-term trend mode. The first one is inherent to the tropical Pacific sea surface temperature forcing, such as the El Nino-Southern Oscillation, whereas the second one is closely associated with global warming. In general, the PrC modes can better characterize the predictability in T1 than in T2, in particular for the inter-annual variability mode in the fall. The prediction skill against observations is better measured by the PrC analysis than by principal component analysis for all seasons, indicating the stronger capability of PrCA in extracting prediction targets.
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The European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VAREPS) is a system designed to provide skilful predictions of small-scale, severe-weather events in the early forecast...
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The European Centre for Medium-Range Weather Forecasts (ECMWF) Variable Resolution Ensemble Prediction System (VAREPS) is a system designed to provide skilful predictions of small-scale, severe-weather events in the early forecast range, and accuratelarge-scale forecast guidance in the extended forecast range (say beyond forecast day 7). In this work, first the rationale behind VAREPS is presented, and then the performance of VAREPS with a truncation at forecast day 7, i.e. T_L399L40(dO-7) and T_L255L40(d7-15), is discussed and compared to the performance of two constant resolution systems, a T_L255L40 and a T_L319L40 (this latter one requires similar computing resources to VAREPS). Average results based on up to 111 cases indicate that VAREPS hasa higher forecast-time-integrated skill, and it provides better forecasts in the early forecast range without losing accuracy in the long forecast range. In the early forecast range, the differences in forecast performance can be very large and responsible for substantial improvements in the prediction of weather variables such as surface wind, significant wave height and total precipitation, as was shown in two case-studies. Average results have also shown that the VAREPS extension to 15 days (the oldEPS system was run operationally only up to forecast day 10) will provide users with some skilful extended-range forecasts.
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Abstract Understanding the relationship between probabilistic and deterministic predictabilities is important for climate predictability studies. Focusing on the actual skill of dynamical seasonal prediction, we previously found t...
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Abstract Understanding the relationship between probabilistic and deterministic predictabilities is important for climate predictability studies. Focusing on the actual skill of dynamical seasonal prediction, we previously found that the probabilistic skills of resolution and relative operating characteristic (ROC)/discrimination, but not reliability, have functional relationships with deterministic anomaly correlation (AC). Herein, we further investigate the relationship between probabilistic and deterministic seasonal potential predictabilities. The potential predictabilities are characterized by the potential skills of the AC, resolution, and ROC evaluated using the perfect-model framework, under which reliability is ideal and not considered. A theoretical argument demonstrates that similar theoretical relationships to those for actual skills exist between probabilistic and deterministic potential predictabilities, regardless of how different the potential predictabilities are from the corresponding actual skills. These theoretical relationships are strictly monotonic and characterized by symmetrical probabilistic predictabilities for the below- and above-normal categories, and lower predictability for the near-normal category corresponding to deterministic predictability. A subsequent diagnostic analysis reveals that while the probabilistic and deterministic potential predictabilities in current dynamical climate models differ noticeably from the corresponding actual skills, they exhibit quasi-monotonic relationships as expected theoretically, which effectively and quantitatively validates the theoretical argument. This work, combined with our previous findings, establishes a solid equivalence of the resolution and discrimination aspects of probabilistic predictability to deterministic predictability in seasonal prediction, which can have beneficial implications for further studying probabilistic predictability.
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