共查询到20条相似文献,搜索用时 15 毫秒
1.
Forecast skill as a function of time lead and time averaging is examined in two 6-member ensembles of seasonal hindcasts.
One ensemble is produced with the second generation general circulation model of the Canadian Centre for Climate Modelling
and Analysis (GCM2) and the other with a reduced resolution version of the numerical weather prediction model of the Canadian
Meteorological Centre (SEF). The integrations are initiated from the NCEP/NCAR reanalyzed data. Monthly sea surface temperature
anomalies observed prior to the forecast period are maintained throughout the forecast season. A statistical forecast improvement
technique, based on the singular value decomposition of forecast and reanalyzed fields, is discussed and evaluated. A simple
analogue of the hindcast integrations is used to examine the behavior of two common skill scores, the correlation skill score
and the explained variance skill score. The maximal skill score and the corresponding optimal forecast in this analogue are
identified. The total skill of the optimal forecast is a sum of two terms, one associated with the initial conditions and
the other with the lower boundary forcing. The two sources of skill operate on different time scales, with initial conditions
being more important in the first one-two weeks and the atmospheric response to the boundary forcing becoming more dominant
for longer time leads and time averages. This suggests that these sources of skill should be considered separately in forecast
optimization. The statistical technique is moderately successful in improving the skill of monthly to seasonal forecasts of
500 hPa height (Z
500) and 700 hPa temperature (T
700) in the Northern Hemisphere and in the North Pacific/North America sector. The improvement is better when the forecasts for
the first week and for the rest of the season are optimized separately. The SEF model produces better Z
500 and T
700 forecasts than GCM2 in the first one-two weeks whereas GCM2 performs slightly better at longer time leads. The skill of zero
time lead forecast decays rapidly with averaging interval for time averages up to about 30–45 days and stabilizes, or even
rises, for longer time averages. Excluding the first week from seasonal forecasts results in substantial degradation of predictive
skill.
Received: 1 November 1999 / Accepted: 24 May 2000 相似文献
2.
K. K. W. Cheung 《Meteorology and Atmospheric Physics》2001,78(1-2):23-34
Summary
Random perturbations (RPs) and a modified version for breeding of growing modes are used with a regional baroclinic mesoscale
model to perform ensemble forecasting of tropical cyclone motion. Based on a sample of six cases, similar conclusions are
found as in previous barotropic modeling studies. Even after introducing a larger spatial correlation into the RPs using a
multi-quadric analysis scheme, the skill of this ensemble mean track prediction is almost always lower than that of the control
forecast in the cases considered. The track prediction performance of the ensemble using regional bred modes (RBMs) as perturbations
has a higher average skill. At nearly all forecast intervals except less than 24 h when the initial position error still dominates,
the ensemble mean tracks in all six cases are improved over the control forecast. In the 6 h–24 h range, the success rate
(ratio of the cases with a forecast improvement to the total number of cases) has a value of 10/24. In the 30 h–48 h range,
the success rate increases to 20/24, but drops to 18/24 in the 54 h–72 h range. A relative skill score (RSS) is used to compare
the skills of the two perturbation methodologies. It is found that the average RSSs of using RBMs are significantly higher
than the corresponding ones of RPs at the 99% confidence level in all three 24-h periods. Note that the above conclusion is
only based on ensemble mean forecasts. All of the possibilities from an ensemble-based probabilistic track distribution are
not explored in this paper. The ensemble spreads in these RBM ensembles are large enough to include the verifying tracks in
all the cases considered. It is also found that the ensemble spread is well correlated with the average error in an ensemble
when using RBMs, but not with the ensemble mean forecast error in both methodologies.
Received February 7, 2001/Revised April 18, 2001 相似文献
3.
R. I. C. C. Francis A. B. Mullan J. A. Renwick 《Theoretical and Applied Climatology》2003,75(1-2):1-14
Summary ?We evaluate United Kingdom Meteorological Office (UKMO) one-month ensemble forecasts of mean sea-level pressure (MSLP) in
the southern hemisphere (SH) to 60° S, with a special focus on their utility near New Zealand (NZ). There are 105 9-member
ensembles, at approximately two-week intervals, between 1995 and 1999. Each forecast is averaged over two successive 15-day
periods and verified against the NCEP/NCAR reanalysis data set. Compared to climatology, the skill of the ensemble mean is
slightly positive in days 1–15, and slightly negative in days 16–30. Skill near NZ is slightly lower than the SH averages.
For SH-scale circulation patterns (as seen in the first few principal components), skill is greater than for most individual
grid points, but is still negligible or negative in days 16–30. Moderate skill-spread correlations (ρ ≈−0.5) were found for
some skill scores. The way that skill varies with season and the Southern Oscillation Index is consistent with other research
but not statistically significant for this small data set. Probabilistic forecasts of low and high pressures have skill similar
to that of the ensemble mean. The ensemble spread is generally too small, in that the analysis lies within the ensemble less
often than the theoretically optimum value of 80% of the time. Measured as a fraction of the natural variability, the spread
increases substantially with time and latitude: it is less than 0.5 near the equator in days 1–15, and takes values near 1
only at higher latitudes during days 16–30. The initial sequential structure of the ensembles (a consequence of the use of
time lags in their genesis) is still apparent in days 1–15 but has disappeared by days 16–30. Three potential alternatives
to the ensemble mean were all found to have less skill than it.
Received June 17, 2001; revised July 4, 2002; accepted November 22, 2002
Published online March 17, 2003 相似文献
4.
Multi-model seasonal hindcasts over the Euro-Atlantic: skill scores and dynamic features 总被引:2,自引:2,他引:0
A group of multi-model seasonal hindcast experiments for Europe are verified and analysed using as reference the European
Centre for Medium-range Weather Forecasts re-analysis and Xie and Arkin precipitation data. Each model's systematic error
is described. Hindcast skill scores are evaluated computing anomaly correlation coefficients. The values of the scores are
highly dependent on the variable, on the region and on the season considered. Scores are particularly low over Europe for
all seasons, reaching their maximum during winter. The presence of occasional poor hindcasts affects the multi-model ensemble
results substantially. In order to see whether or not the skill inconsistencies are linked to the model's inability to forecast
the evolution of some particular patterns, hindcast skill scores are computed for the four large-scale patterns which explain
most of the observed low-frequency variance over the Euro-Atlantic region, during winter. These scores are strongly dependent
on the pattern. Multi-model hindcasts are better than the best single model hindcast only for those patterns for which the
model biases cancel each other. In all cases, substantially better multi-model hindcast scores for all patterns can be obtained
by combining the four model results using optimal weights, computed for each model and for each pattern with the technique
suggested by Thompson. All results show no dependence on the ensemble size considered. Skill scores are finally computed for
several indices, which measure the variability of selected weather regimes over Europe. Regimes scores are consistent with
the scores obtained for the correspondent Euro-Atlantic EOF patterns, and it is shown that the removal of each model's systematic
error from its hindcasts does not improve the final regime hindcast skill.
Received: 5 February 1999 / Accepted: 14 December 1999 相似文献
5.
We examine the Florida Climate Institute–Florida State University Seasonal Hindcast (FISH50) skill at a relatively high (50 km grid) resolution two tiered Atmospheric General Circulation Model (AGCM) for boreal winter and spring seasons at zero and one season lead respectively. The AGCM in FISH50 is forced with bias corrected forecast sea surface temperature averaged from two dynamical coupled ocean–atmosphere models. The comparison of the hindcast skills of precipitation and surface temperature from FISH50 with the coupled ocean–atmosphere models reveals that the probabilistic skill is nearly comparable in the two types of forecast systems (with some improvements in FISH50 outside of the global tropics). Furthermore the drop in skill in going from zero lead (boreal winter) to one season lead (boreal spring) is also similar in FISH50 and the coupled ocean–atmosphere models. Both the forecast systems also show that surface temperature hindcasts have more skill than the precipitation hindcasts and that land based precipitation hindcasts have slightly lower skill than the corresponding hindcasts over the ocean. 相似文献
6.
Harun A. Rashid Harry H. Hendon Matthew C. Wheeler Oscar Alves 《Climate Dynamics》2011,36(3-4):649-661
Predictions of the Madden?CJulian oscillation (MJO) are assessed using a 10-member ensemble of hindcasts from POAMA, the Australian Bureau of Meteorology coupled ocean?Catmosphere seasonal prediction system. The ensemble of hindcasts was initialised from observed atmosphere and ocean initial conditions on the first of each month during 1980?C2006. The MJO is diagnosed using the Wheeler-Hendon Real-time Multivariate MJO (RMM) index, which involves projection of daily data onto the leading pair of eigenmodes from an analysis of zonal winds at 200 and 850?hPa and outgoing longwave radiation (OLR) averaged about the equator. Forecasts of the two component (RMM1 and RMM2) index are quantitatively compared with observed behaviour derived from NCEP reanalyses and satellite OLR using the bivariate correlation skill, root-mean-square error (RMSE), and measures of the MJO amplitude and phase error. Comparison is also made with a simple vector autoregressive (VAR) prediction model of RMM as a benchmark. Using the full hindcast set, we find that the MJO can be predicted with the POAMA ensemble out to about 21?days as measured by the bivariate correlation exceeding 0.5 and the bivariate RMSE remaining below ~1.4 (which is the value for a climatological forecast). The VAR model, by comparison, drops to a correlation of 0.5 by about 12?days. The prediction limit from POAMA increases by less than 2?days for times when the MJO has large initial amplitude, and has little sensitivity to the initial phase of the MJO. The VAR model, on the other hand, shows a somewhat larger increase in skill for times of strong MJO variability and has greater sensitivity to initial phase, with lower skill for times when MJO convection is developing in the Indian Ocean. The sensitivity to season is, however, greater for POAMA, with maximum skill occurring in the December?CJanuary?CFebruary season and minimum skill in June?CJuly?CAugust. Examination of the MJO amplitudes shows that individual POAMA members have slightly above observed amplitude after a spin-up of about 10?days, whereas examination of the MJO phase error reveals that the model has a consistent tendency to propagate the MJO slightly slower than observed. Finally, an estimate of potential predictability of the MJO in POAMA hindcasts suggests that actual MJO prediction skill may be further improved through continued development of the dynamical prediction system. 相似文献
7.
M. A. Tadross W. J. Gutowski Jr B. C. Hewitson C. Jack M. New 《Theoretical and Applied Climatology》2006,86(1-4):63-80
Summary Two cumulus convection and two planetary boundary layer schemes are used to investigate the climate of southern Africa using
the MM5 regional climate model. Both a wet (1988/89) and a dry (1991/92) summer (December–February, DJF) rainfall season are
simulated and the results compared with three different observational sources: Climate Research Unit seasonal data (precipitation,
2 m surface temperature, number of rain days), satellite-derived diurnal precipitation and the Surface Radiation Budget diurnal
short-wave fluxes and optical depth.
Using the ETA model boundary layer in MM5 simulates too much incident short-wave radiation at the surface at 12 UTC, whereas
the medium range forecast model boundary layer yields a diurnal cycle of short-wave radiation closer to the observed. The
Betts-Miller convection scheme in MM5 simulates peak rainfall later in the day and less rain days than observed, whereas when
using the Kain-Fritsch convection scheme a peak rainfall earlier in the day and more rain days than observed are simulated.
The intensity of the hydrological cycle is therefore dependent on the choice of convection scheme, which in turn is further
modified by the boundary layer scheme. Precipitation during the wet 1988/89 season is reasonably captured by most simulations,
though using the Betts-Miller scheme more accurately simulates rainfall during the dry 1991/92 season. Mean DJF biases in
the surface temperature and diurnal temperature range are consistent with biases in the number of rain days and the diurnal
cycles of surface moisture and energy. 相似文献
8.
Probabilistic prediction of Indian summer monsoon rainfall using global climate models 总被引:1,自引:0,他引:1
Makarand A. Kulkarni Nachiketa Acharya Sarat C. Kar U. C. Mohanty Michael K. Tippett Andrew W. Robertson Jing-Jia Luo Toshio Yamagata 《Theoretical and Applied Climatology》2012,107(3-4):441-450
Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low. 相似文献
9.
《大气与海洋》2013,51(3):204-223
Abstract The performance of seasonal hindcasts produced with four global atmospheric models in the second phase of the Canadian Historical Forecasting Project is evaluated. Deterministic and probabilistic forecast skill assessments are carried out using common verification measures. Several methods of combining multi‐model output to produce deterministic and probabilistic forecasts of near‐surface air temperature, 500 hPa geopotential height, and 700 hPa temperature for zero‐month and one‐month leads are considered. A variance‐based weighting modestly improves the skill of deterministic and probabilistic hindcasts in some cases. A parametric Gaussian probability estimator is superior to a non‐parametric count‐method estimator for producing multi‐model probability forecasts. Statistical adjustment is beneficial for deterministic and probabilistic hindcasts of near‐surface temperature over the ocean but not always over land. Skill improves with the number of different models used for a given total ensemble size. The four‐model ensemble is shown to be a reasonable multi‐model configuration. 相似文献
10.
Predicting US summer precipitation using NCEP Climate Forecast System version 2 initialized by multiple ocean analyses 总被引:1,自引:0,他引:1
Jieshun Zhu Bohua Huang Zeng-Zhen Hu James L. Kinter III Lawrence Marx 《Climate Dynamics》2013,41(7-8):1941-1954
This study examines the prediction skill of the contiguous United States (CONUS) precipitation in summer, as well as its potential sources using a set of ensemble hindcasts conducted with the National Centers for Environmental Prediction (NCEP) Climate Forecast System version 2 and initialized from four independent ocean analyses. The multiple ocean ensemble mean (MOCN_ESMEAN) hindcasts start from each April for 26 summers (1982–2007), with each oceanic state paired with four atmosphere-land states. A subset of hindcasts from the NCEP CFS Reanalysis and Reforecast (CFSRR) project for the same period, from the same initial month and with the same total ensemble size, is also analyzed. Compared with CFSRR, MOCN_ESMEAN is distinguished by its oceanic ensemble spread that introduces potentially larger perturbations and better spatial representation of the oceanic uncertainty. The prediction skill of the CONUS precipitation in summer shows a similar spatial pattern in both MOCN_ESMEAN and CFSRR, but the results suggested that initialization from multiple ocean analyses may bring more robust signals and additional skills to the seasonal prediction for both sea surface temperature and precipitation. Among the predictable areas for precipitation, the northwestern CONUS (NWUS) is the most robust. A further analysis shows that the enhanced summer precipitation prediction skill in NWUS is mainly associated with the El Niño/Southern Oscillation, with possible influence also from the Pacific Decadal Oscillation. Through this work, we argue that a large ensemble is necessary for precipitation forecast in mid-latitudes, such as the CONUS, and taking into account of the oceanic initial state uncertainty is an efficient way to build such an ensemble. 相似文献
11.
Decadal prediction skill in a multi-model ensemble 总被引:4,自引:3,他引:1
Geert Jan van Oldenborgh Francisco J. Doblas-Reyes Bert Wouters Wilco Hazeleger 《Climate Dynamics》2012,38(7-8):1263-1280
Decadal climate predictions may have skill due to predictable components in boundary conditions (mainly greenhouse gas concentrations but also tropospheric and stratospheric aerosol distributions) and initial conditions (mainly the ocean state). We investigate the skill of temperature and precipitation hindcasts from a multi-model ensemble of four climate forecast systems based on coupled ocean-atmosphere models. Regional variations in skill with and without trend are compared with similarly analysed uninitialised experiments to separate the trend due to monotonically increasing forcings from fluctuations around the trend due to the ocean initial state and aerosol forcings. In temperature most of the skill in both multi-model ensembles comes from the externally forced trends. The rise of the global mean temperature is represented well in the initialised hindcasts, but variations around the trend show little skill beyond the first year due to the absence of volcanic aerosols in the hindcasts and the unpredictability of ENSO. The models have non-trivial skill in hindcasts of North Atlantic sea surface temperature beyond the trend. This skill is highest in the northern North Atlantic in initialised experiments and in the subtropical North Atlantic in uninitialised simulations. A similar result is found in the Pacific Ocean, although the signal is less clear. The uninitialised simulations have good skill beyond the trend in the western North Pacific. The initialised experiments show some skill in the decadal ENSO region in the eastern Pacific, in agreement with previous studies. However, the results in this study are not statistically significant (p?≈?0.1) by themselves. The initialised models also show some skill in forecasting 4-year mean Sahel rainfall at lead times of 1 and 5?years, in agreement with the observed teleconnection from the Atlantic Ocean. Again, the skill is not statistically significant (p?≈?0.2). Furthermore, uninitialised simulations that include volcanic aerosols have similar skill. It is therefore still an open question whether initialisation improves predictions of Sahel rainfall. We conclude that the main source of skill in forecasting temperature is the trend forced by rising greenhouse gas concentrations. The ocean initial state contributes to skill in some regions, but variations in boundary forcings such as aerosols are as important in decadal forecasting. 相似文献
12.
L. Goddard A. Kumar A. Solomon D. Smith G. Boer P. Gonzalez V. Kharin W. Merryfield C. Deser S. J. Mason B. P. Kirtman R. Msadek R. Sutton E. Hawkins T. Fricker G. Hegerl C. A. T. Ferro D. B. Stephenson G. A. Meehl T. Stockdale R. Burgman A. M. Greene Y. Kushnir M. Newman J. Carton I. Fukumori T. Delworth 《Climate Dynamics》2013,40(1-2):245-272
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty. 相似文献
13.
Interannual variability and predictability in an ensemble of AMIP climate simulations conducted with the CCC GCM2 总被引:2,自引:0,他引:2
F. W. Zwiers 《Climate Dynamics》1996,12(12):825-847
This study considers an ensemble of six 10-year climate simulations conducted with the Canadian Climate Centre 2nd generation
General Circulation Model (CCC GCM2). Each simulation was forced according to the Atmospheric Model Intercomparison Project
(AMIP) experimental protocol using monthly mean sea surface temperatures and sea-ice extents based on observations for January,
1979 to December 1988. One simulation, conducted on a CRAY computer, was initiated from analysed 1 January 1979 conditions
while the remaining 5 simulations, conducted on a NEC computer, were initiated from previously simulated model states obtained
from a long control integration. The interannual variability and potential predictability of simulated and observed 500 hPa
geopotential, 850 hPa temperature and 300 hPa stream function are examined and inter-compared using statistical analysis of
variance techniques to partition variance into a number of components. The boundary conditions specified by AMIP are found
to induce statistically significant amounts of predictable variance on the interannual time scale in the tropics and, to a
lesser extent, at extratropical latitudes. In addition, local interactions between the atmosphere and the land surface apparently
induce significant amounts of potentially predictable interannual variance in the tropical lower atmosphere and also at some
locations in the temperate lower atmosphere. No evidence was found that the atmosphere's internal dynamics on their own generate
potentially predictable variations on the interannual time scale. The sensitivity of the statistical methods used is demonstrated
by the fact that we are able to detect differences between the climates simulated on the two computers used. The causes of
these physically insignificant changes are traced. The statistical procedures are checked by confirming that the choice of
initial conditions does not lead to significant inter-simulation variation. The simulations are also interpreted as an ensemble
of climate forecasts that rely only on the specified boundary conditions for their predictive skill. The forecasts are verified
against observations and against themselves. In agreement with other studies it was found that the forecasts have very high
skill in the tropics and moderate skill in the extratropics.
Received: 18 December 1995 / Accepted: 4 April 1996 相似文献
14.
K. Prasad 《Meteorology and Atmospheric Physics》2006,91(1-4):183-199
Summary Results of an earlier study of cyclone track prediction using a quasi-Lagrangian model (QLM) to generate track forecasts of
up to 36 hours were reported by Prasad and Rama Rao (2003). Further experiments to produce track forecasts of up to 72 hours
with an updated version of the same model have been carried out in the present study. In this case, the ability of the model
to predict recent historical cyclones in the Bay of Bengal and Arabian Sea has been assessed. Analysis of some of the structural
features of analyzed and predicted fields has been carried out. Such fields include wind distribution and vertical motion
around the cyclone centre. In addition, the merging of an idealized vortex with the large scale initial fields provided by
a global model, has been carried out for a particular case study of a May 1997 storm, which hit the Bangladesh coast. This
current study has demonstrated that the model generates a realistic structure of a tropical cyclone with an idealized vortex.
Performance evaluation has been carried out by computing the direct position errors (DPE). The results of which show that
the mean error for a 24 h forecast is about 122 km, which increases to about 256 km for a 48 h forecast and 286 km for a 72 h
forecast. These figures are comparable to similar errors in respect of tropical cyclone forecasts produced by an advanced
NWP centre, viz., the UKMO global model during the corresponding period, 1997–2000 (obtained from UKMO web site). The average
forecast errors of the UKMO model are 160 km for 24 h, 265 km for 48 h, 415 km for 72 h forecast ranges. 相似文献
15.
Anushiya Jeganathan Ramachandran Andimuthu 《Theoretical and Applied Climatology》2013,114(3-4):705-714
This paper describes the projection of climate change scenarios under increased greenhouse gas emissions, using the results of atmospheric-ocean general circulation models in the Coupled Model Intercomparison Project phase 3 dataset. A score is given to every model based on global and regional performance. Four out of 20 general circulation models (GCMs) were selected based on skill in predicting observed annual temperature and precipitation conditions. The ensemble of these four models shows superiority over the individual model scores. These models were subjected to increases in future anthropogenic radiative forcings for constructing climate change scenarios. Future climate scenarios for Tamil Nadu were developed with MAGICC/SCENGEN software. Model results show both temperature and precipitation increases under increased greenhouse gas scenarios. Northeast and northwest parts of Tamil Nadu show a greater increase in temperature and precipitation. Seasonally, the maximum rise in temperature occurred during the MAM season, followed by DJF, JJA, and SON. Decreasing trends of precipitation were observed during DJF and MAM. 相似文献
16.
New Models for Long Range Forecasts of Summer Monsoon Rainfall over North West and Peninsular India 总被引:1,自引:0,他引:1
M. Rajeevan Pulak Guhathakurta V. Thapliyal 《Meteorology and Atmospheric Physics》2000,73(3-4):211-225
Summary New models based on (a) Multivariate Principal Component Regression (PCR) (b) Neural Network (NN) and (c) Linear Discriminant
Analysis (LDA) techniques were developed for long-range forecasts of summer monsoon (June–September) rainfall over two homogeneous
regions of India, viz., North West India and Peninsular India. The PCR and NN models were developed with two different data
sets. One set consisted 42 years (1958–1999) of data with 8 predictors and the other, 49 years (1951–1999) of data with 6
predictors. The predictors were subjected to the Principal Component Analysis (PCA) before model development. Two different
neural networks were designed with 2 and 3 hidden neurons. To avoid the nonlinear instability, 20 ensemble runs were made
while training the network and the ensemble mean results are discussed. The LDA model was developed with 42 years of data
(1958–1999) for classifying three rainfall intervals with equal prior probability of 0.33. Both the PCR and NN models showed
useful forecast skill for NW India and Peninsular India. Models with 8 predictors performed better than the models with only
6 predictors. The NN model with 3 hidden neurons performed better than model with 2 hidden neurons. For NW India, the NN model
performed better than the PCR model. The RMSE of the NN model and PCR model with 8 predictors for NW India (Peninsular India)
during the independent period 1984–99 was 12.5% (12.2%) and 12.6% (11.5%), respectively. Corresponding figures for the models
with 6 predictors are 15.0% (13.0%) and 13.9% (11.4%) respectively. During the independent period, model errors were large
in 1991, 1994, 1997 and 1999. However all the models showed deteriorating predictive skill after 1988, both for NW India and
Peninsular India. The LDA model correctly classified 62% of grouped cases for NW India and Peninsular India. The LDA model
showed better skill in classifying deficient rainfall (< − 8%) over NW India and excess rainfall (> 3%) over Peninsular India.
Received October 2, 1999 Revised December 28, 1999 相似文献
17.
Summary A revised 25-point Shuman-Shapiro Spatial Filter (RSSSF) has been applied to six atmospheric circulation models and multi-model
ensemble (MME) predictions, and its effect on the improvement of model forecast skill scores of the Asian summer precipitation
anomaly is discussed in this paper. On the basis of 21-yr model ensemble predictions, the RSSSF can remove the unpredictable
‘noise’ with respect to the 2-grid wavelength in the model precipitation anomaly fields and maintain the large-scale counterpart,
which is related to the response of the model to large-scale boundary forcing. Therefore, this could possibly enhance the
forecast skill of the Asian summer rainfall anomaly in the models and the MME. The potential improvement of model forecasting
skill is found in the Asian summer monsoon region, where the anomaly correlation coefficient (ACC) has been improved by 7–40%,
corresponding to the decreased root mean square error (RMSE) in the model and the MME precipitation anomaly forecasts. 相似文献
18.
《Atmospheric Research》2010,95(4):684-693
The numerical weather prediction model LM COSMO was employed to study the regional ensemble forecast of convective precipitation. The relationship between ensemble spread and ensemble skill and the possibility of estimating ensemble skill on the basis of ensemble spread were investigated. Five convective events that produced heavy local rainfall in the Czech Republic were studied. The LM COSMO was run with a horizontal resolution of 2.8 km and an ensemble of 13 forecasts was created by modifying the initial and boundary conditions. Forecasts were verified by gauge-adjusted radar-based rainfalls. Ensemble skill and ensemble spread were determined using the Fractions Skill Score (FSS), which depended on the scale of the elementary area and on a precipitation threshold. The spread represents the differences between the control forecast and the forecasts provided by each ensemble member, while the skill evaluates the difference between the precipitation forecast and radar-based rainfalls. In this study, the ensemble skill is estimated on the basis of the ensemble spread. The numerical experiments used the FSS-based skill and spread values related to four events to estimate the skill–spread relationship. The relationship was applied to a fifth event to estimate the QPF ensemble skill given the ensemble FSS-based spread. The evaluation was performed separately for 1, 3, and 6 h rainfalls using various threshold values and scales. The absolute frequencies of the differences between diagnostic and prognostic FSS-based skill show that all of the distributions have means and medians close to zero and that the interquartile ranges are between 0.10 and 0.30. The results indicate that 67% of all the fitted FSS-skill values were within 0.15 of the true values. One of five events showed a marked overestimation of the prognostic FSS-skill so that only 39% of skill values were fitted. At the other four events, the 75% of predicted FSS-skill values were in the range of 0.15 of the diagnosed FSS-skill. The results appear to be encouraging; however, tests with more extended data are needed to confirm the potential of the technique. 相似文献
19.
D. R. Pattanaik U. C. Mohanty H. R. Hatwar G. Srinivasan Y. V. Ramarao P. Sinha A. Brookshaw 《Theoretical and Applied Climatology》2008,92(3-4):165-179
Summary A comparative study was performed to evaluate the performance of the UK Met Office’s Global Seasonal (GloSea) prediction General
Circulation Model (GCM) for the forecast of maximum surface air temperature (Tmax) over the Indian region using the model generated hindcast of 15-members ensemble for 16 years (1987–2002). Each hindcast
starts from 1st January and extends for a period of six months in each year. The model hindcast Tmax is compared with Tmax obtained from verification analysis during the hot weather season on monthly and seasonal scales from March to June.
The monthly and seasonal model hindcast climatology of Tmax from 240 members during March to June and the corresponding observed climatology show highly significant (above 99.9% level)
correlation coefficients (CC) although the hindcast Tmax is over-estimated (warm bias) over most parts of the Indian region. At the station level over New Delhi, although the forecast
error (forecast-observed) at the monthly scale gradually increases from March to June, the forecast error at the seasonal
scale during March to May (MAM) is found to be just 1.67 °C. The GloSea model also simulates well Tmax anomalies on monthly and seasonal scales during March to June with the lower Root Mean Square Error (RMSE) of bias corrected
forecast (less than 1.2 °C), which is much less than the corresponding RMSE of climatology (reference) forecast. The anomaly
CCs (ACCs) over the station in New Delhi are also highly significant (above 95% level) on monthly to seasonal time scales
from March to June, except for April.
The skill of the GloSea model for the seasonal forecast of Tmax as measured from the ACC map and the bias corrected RMSE map is reasonably good during MAM and April to June (AMJ) with higher
ACC (significant at 95% level) and lower RMSE (less than 1.5 °C) found over many parts of the Indian regions.
Authors’ addresses: D. R. Pattanaik, H. R. Hatwar, G. Srinivasan, Y. V. Ramarao, India Meteorological Department (IMD), New
Delhi, India; U. C. Mohanty, P. Sinha, Centre for Atmospheric Sciences, Indian Institute of Technology, Hauz Khas, New Delhi
110016, India; Anca Brookshaw, UK Met Office, UK. 相似文献
20.
利用欧洲中期天气预报中心 (ECMWF)、美国大气环境预报中心 (NCEP) 集合预报系统 (EPS) 降水量预报资料,CMORPH (NOAA Climate Prediction Center Morphing Method) 卫星与全国3万个自动气象站降水量融合资料,基于技巧评分、ROC (relative operating characteristic) 分析等方法,对比两个集合预报系统对秦岭及周边地区的降水预报性能。结果表明:两个系统均能较好表现降水量的空间形态,对于不同量级降水,ECMWF集合预报系统0~240 h控制及扰动预报优于NCEP集合预报系统,但NCEP集合预报系统264~360 h预报时效整体表现更好; ECMWF集合预报系统0~120 h大雨集合平均优于NCEP集合预报系统,两个系统集合平均的预报技巧整体低于其控制及扰动成员预报,这种现象ECMWF集合预报系统表现更为显著; ECMWF集合预报系统降水预报概率优于NCEP集合预报系统。ROC分析显示,随着预报概率的增大,ECMWF集合预报系统在命中率略微下降的情况下,显著减小了空报率,NCEP集合预报系统则表现出高空报、高命中率。 相似文献