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1.
An abnormal warming condition with 3?C5?°C rise in temperature above its normal value was observed in the Indian state of Odisha during 12?C16 November 2009. This study aims at examining the impact of additional weather observations obtained from the automatic weather stations (AWS) installed in the recent past on the numerical simulation of such abnormal warming. AWS observations, such as temperature at 2?m (T2m), dew point temperature at 2?m (Td2m), wind vector at 10?m (speed and direction), and sea level pressure (SLP) have been assimilated into the state-of-the-art Weather Research and Forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). Six sets of experiments have been conducted here. There is no data assimilation in the control experiment, whereas AWS and radiosonde observations have been assimilated in rest of the five experiments. The model integrations have been made for 72?h in each experiment starting from 0000 UTC November 12 to 0000 UTC November 15, 2009. Assimilation experiments have also been performed to assess the impact of individual surface parameters on the model simulations. Impact of AWS observations on model simulation has been examined with reference to the control simulation and quantified in terms of root-mean-square error and forecast skill score for temperature, sea level pressure, and relative humidity at three selected stations Bonaigarh, Brahmagiri, and Nuapada in Odisha. Results indicate improvements in the surface air temperature and SLP simulations in the timescale of 72?h at all the three stations due to additional weather data assimilation into the model. Improvements in simulation are significant up to 24?h. The assimilation of additional wind fields significantly improved the temperature simulation at all the three stations. The simulated SLP has also improved significantly due to the assimilation of surface temperature and moisture. 相似文献
2.
The present study explored the effect of assimilation of Advanced TIROS Vertical Sounder (ATOVS) temperature and humidity
profiles and Spectral sensor microwave imager (SSM/I) total precipitable water (TPW) on the simulation of a monsoon depression
which formed over the Arabian Sea during September 2005 using the Weather Research and Forecast model. The three-dimensional
variational (3DVAR) data assimilation technique has been employed for the purpose of assimilation of satellite observations.
Statistical scores like “equitable threat score,” “bias score,” “forecast impact,” and “improvement parameter” have been used
to examine the impact of the above-mentioned satellite observations on the numerical simulation of a monsoon depression. The
diagnostics of this study include verification of the vertical structure of depression, in terms of temperature anomaly profiles
and relative vorticity profiles with observations/analysis. Additional diagnostics of the study include the analysis of the
heat budget and moisture budget. Such budget studies have been performed to provide information on the role of cumulus convection
associated with the depression. The results of this study show direct and good evidence of the impact of the assimilation
of the satellite observations using 3DVAR on the dynamical and thermodynamical features of a monsoon depression along with
the effect of inclusion of satellite observation on the spatial pattern of the simulated precipitation associated with the
depression. The “forecast impact” parameter calculated for the wind speed provides good evidence of the positive impact of
the assimilation of ATOVS temperature and humidity profiles and SSM/I TPW on the model simulation, with the assimilation of
the ATOVS profiles showing better impact in terms of a more positive value of the “forecast impact” parameter. The results
of the study also indicate the improvement of the forecast skill in terms of “equitable threat score” and “bias score” due
to the assimilation of satellite observation. 相似文献
3.
C. V. Srinivas V. Yesubabu K. B. R. R. Hariprasad S. S. V. Ramakrishna B. Venkatraman 《Natural Hazards》2013,65(1):331-357
Real-time predictions for the JAL severe cyclone formed in November 2010 over Bay of Bengal using a high-resolution Weather Research and Forecasting (WRF ARW) mesoscale model are presented. The predictions are evaluated with different initial conditions and assimilation of observations. The model is configured with two-way interactive nested domains and with fine resolution of 9?km for the region covering the Bay of Bengal. Simulations are performed with NCEP GFS 0.5° analysis and forecasts for initial/boundary conditions. To examine the impact of initial conditions on the forecasts, eleven real-time numerical experiments are conducted with model integration starting at 00, 06, 12, 18 UTC 4 Nov, 5?Nov and 00, 06, 12 UTC 6 Nov and all ending at 00 UTC 8 Nov. Results indicated that experiments starting prior to 18 UTC 04 Nov produced faster moving cyclones with higher intensity relative to the IMD estimates. The experiments with initial time at 18 UTC 04 Nov, 00 UTC 05 Nov and with integration length of 78?h and 72?h produced best prediction comparable with IMD estimates of the cyclone track and intensity parameters. To study the impact of observational assimilation on the model predictions FDDA, grid nudging is performed separately using (1) land-based automated weather stations (FDDAAWS), (2) MODIS temperature and humidity profiles (FDDAMODIS), and (3) ASCAT and OCEANSAT wind vectors (FDDAASCAT). These experiments reduced the pre-deepening period of the storm by 12?h and produced an early intensification. While the assimilation of AWS data has shown meagre impact on intensity, the assimilation of scatterometer winds produced an intermittent drop in intensity in the peak stage. The experiments FDDAMODIS and FDDAQSCAT produced minimum error in track and intensity estimates for a 90-h prediction of the storm. 相似文献
4.
Impact of satellite soundings on the simulation of heavy rainfall associated with tropical depressions 总被引:1,自引:1,他引:0
The present study is carried out to examine the impact of temperature and humidity profiles from moderate resolution imaging
spectroradiometer (MODIS) or/and atmospheric infrared sounder (AIRS) on the numerical simulation of heavy rainfall events
over the India. The Pennsylvania State University–National Centre for Atmospheric Research fifth-generation mesoscale model
(MM5) and its three-dimensional variational (3D-Var) assimilation technique is used for the numerical simulations. The heavy
rainfall events occurred during October 26–29, 2005, and October 27–30, 2006, were chosen for the numerical simulations. The
results showed that there were large differences observed in the initial meteorological fields from control experiment (CNT;
without satellite data) and assimilation experiments (MODIS (assimilating MODIS data), AIRS; (assimilating AIRS data); BOTH
(assimilating MODIS and AIRS data together)). The assimilation of satellite data (MODIS, AIRS, and BOTH) improved the predicted
thermal and moisture structure of the atmosphere when compared to CNT. Among the experiments, the predicted track of tropical
depressions from MODIS was closer to the observed track. Assimilation of MODIS data also showed positive impact on the spatial
distribution and intensity of predicted rainfall associated with the depressions. The statistical skill scores obtained for
different experiments showed that assimilation of satellite data (MODIS, AIRS, and BOTH) improved the rainfall prediction
skill when compared to CNT. Root mean square error in quantitative rainfall prediction is less in the experiment which assimilated
MODIS data when compared to other experiments. 相似文献
5.
This study aims to present an encouraging example of prediction of super cyclone Gonu over the northern Indian Ocean in 2007. A series of experiments are conducted using the advanced Weather Research and Forecasting model with three-dimensional variational method to assimilate GPS RO refractivity from FORMOSAT-3/COSMIC (hereafter referred as GPS) and radiosonde sounding (GTS) to highlight the relative impact of GPS RO data on model prediction. Significant differences in cyclone track and intensity prediction are exhibited in various experiments with and without cyclic assimilations. Both cold-start (non-cyclic) and hot-start (cyclic) runs with GPS RO data exhibit improvement on later track prediction compared to the control run without data assimilation. GPS experiment outperforms other experiments including GTS in track prediction with the smallest cross-track error. Sensitivity tests were also conducted to identify which GPS RO sounding gives more impact on track prediction. We found that the sounding closest to the cyclone exhibits the largest contribution to track prediction. Assimilation of the RO soundings in the vicinity of Gonu cyclone appears to modify the environmental conditions that result in a later development of a couplet of high and low pressure, leading to a positive impact on track prediction. Sensitivity experiments indicate that the initial information retrieved by GPS data at upper levels that produce colder temperature increments indeed contributes more improvement to track prediction. 相似文献
6.
Impact of Doppler weather radar data on thunderstorm simulation during STORM pilot phase—2009 总被引:1,自引:1,他引:0
S. Kiran Prasad U. C. Mohanty A. Routray Krishna K. Osuri S. S. V. S. Ramakrishna Dev Niyogi 《Natural Hazards》2014,74(3):1403-1427
This study assesses the impact of Doppler weather radar (DWR) data (reflectivity and radial wind) assimilation on the simulation of severe thunderstorms (STS) events over the Indian monsoon region. Two different events that occurred during the Severe Thunderstorms Observations and Regional Modeling (STORM) pilot phase in 2009 were simulated. Numerical experiments—3DV (assimilation of DWR observations) and CNTL (without data assimilation)—were conducted using the three-dimensional variational data assimilation technique with the Advanced Research Weather Research and Forecasting model (WRF-ARW). The results show that consistent with prior studies the 3DV experiment, initialized by assimilation of DWR observations, performed better than the CNTL experiment over the Indian region. The enhanced performance was a result of improved representation and simulation of wind and moisture fields in the boundary layer at the initial time in the model. Assimilating DWR data caused higher moisture incursion and increased instability, which led to stronger convective activity in the simulations. Overall, the dynamic and thermodynamic features of the two thunderstorms were consistently better simulated after ingesting DWR data, as compared to the CNTL simulation. In the 3DV experiment, higher instability was observed in the analyses of thermodynamic indices and equivalent potential temperature (θ e) fields. Maximum convergence during the mature stage was also noted, consistent with maximum vertical velocities in the assimilation experiment (3DV). In addition, simulated hydrometeor (water vapor mixing ratio, cloud water mixing ratio, and rain water mixing ratio) structures improved with the 3DV experiment, compared to that of CNTL. From the higher equitable threat scores, it is evident that the assimilation of DWR data enhanced the skill in rainfall prediction associated with the STS over the Indian monsoon region. These results add to the body of evidence now which provide consistent and notable improvements in the mesoscale model results over the Indian monsoon region after assimilating DWR fields. 相似文献
7.
In this paper, impact of Indian Doppler Weather Radar (DWR) data, i.e., reflectivity (Z), radial velocity (Vr) data individually and in combination has been examined for simulation of mesoscale features of a land-falling
cyclone with Advance Regional Prediction System (ARPS) Model at 9-km horizontal resolution. The radial velocity and reflectivity
observations from DWR station, Chennai (lat. 13.0°N and long. 80.0°E), are assimilated using the ARPS Data Assimilation System
(ADAS) and cloud analysis scheme of the model. The case selected for this study is the Bay of Bengal tropical cyclone NISHA
of 27–28 November 2008. The study shows that the ARPS model with the assimilation of radial wind and reflectivity observations
of DWR, Chennai, could simulate mesoscale characteristics, such as number of cells, spiral rain band structure, location of
the center and strengthening of the lower tropospheric winds associated with the land-falling cyclone NISHA. The evolution
of 850 hPa wind field super-imposed vorticity reveals that the forecast is improved in terms of the magnitude and direction
of lower tropospheric wind, time, and location of cyclone in the experiment when both radial wind and reflectivity observations
are used. With the assimilation of both radial wind and reflectivity observations, model could reproduce the rainfall pattern
in a more realistic way. The results of this study are found to be very promising toward improving the short-range mesoscale
forecasts. 相似文献
8.
Numerical simulation of cyclonic storms FANOOS, NARGIS with assimilation of conventional and satellite observations using 3-DVAR 总被引:3,自引:2,他引:1
C. V. Srinivas V. Yesubabu K. B. R. R. Hari Prasad B. Venkatraman S. S. V. S. Ramakrishna 《Natural Hazards》2012,63(2):867-889
In this work, the impact of assimilation of conventional and satellite data is studied on the prediction of two cyclonic storms in the Bay of Bengal using the three-dimensional variational data assimilation (3D-VAR) technique. The FANOOS cyclone (December 6?C10, 2005) and the very severe cyclone NARGIS (April 28?CMay 2, 2008) were simulated with a double-nested weather research and forecasting (WRF-ARW) model at a horizontal resolution of 9?km. Three numerical experiments were performed using the WRF model. The back ground error covariance matrix for 3DVAR over the Indian region was generated by running the model for a 30-day period in November 2007. In the control run (CTL), the National Centers for Environmental Prediction (NCEP) global forecast system analysis at 0.5° resolution was used for the initial and boundary conditions. In the second experiment called the VARCON, the conventional surface and upper air observations were used for assimilation. In the third experiment (VARQSCAT), the ocean surface wind vectors from quick scatterometer (QSCAT) were used for assimilation. The CTL and VARCON experiments have produced higher intensity in terms of sea level pressure, winds and vorticity fields but with higher track errors. Assimilation of conventional observations has meager positive impact on the intensity and has led to negative impact on simulated storm tracks. The QSCAT vector winds have given positive impact on the simulations of intensity and track positions of the two storms, the impact is found to be relatively higher for the moderate intense cyclone FANOOS as compared to very severe cyclone NARGIS. 相似文献
9.
Impact of variational assimilation technique on simulation of a heavy rainfall event over Pune,India
V. Yesubabu Sahidul Islam D. R. Sikka Akshara Kaginalkar Sagar Kashid A. K. Srivastava 《Natural Hazards》2014,71(1):639-658
Prediction of heavy rainfall events due to severe convective storms in terms of their spatial and temporal scales is a challenging task for an operational forecaster. The present study is about a record-breaking heavy rainfall event observed in Pune (18°31′N, 73°55′E) on October 4, 2010. The day witnessed highest 24-h accumulated precipitation of 181.3 mm and caused flash floods in the city. The WRF model-based real-time weather system, operating daily at Centre for Development of Advanced Computing using PARAM Yuva supercomputer showed the signature of this convective event 4-h before, but failed to capture the actual peak rainfall and its location with reference to the city’s observational network. To investigate further, five numerical experiments were conducted to check the impact of assimilation of observations in the WRF model forecast. First, a control experiment was conducted with initialization using National Centre for Environmental Prediction (NCEP)’s Global Forecast System 0.5° data, while surface observational data from NCEP Prepbufr system were assimilated in the second experiment (VARSFC). In the third experiment (VARAMV), NCEP Prepbufr atmospheric motion vectors were assimilated. Fourth experiment (VARPRO) was assimilated with conventional soundings data, and all the available NCEP Prepbufr observations were assimilated in the fifth experiment (VARALL). Model runs were compared with observations from automated weather stations (AWS), synoptic charts of Indian Meteorological Department (IMD). Comparison of 24-h accumulated rainfall with IMD AWS 24-h gridded data showed that the fifth experiment (VARALL) produced better picture of heavy rainfall, maximum up to 251 mm/day toward the southern side, 31 km away from Pune’s IMD observatory. It was noticed that the effect of soundings observations experiment (VARPRO) caused heavy precipitation of 210 mm toward the southern side 49 km away from Pune. The wind analysis at 850 and 200 hPa indicated that the surface and atmospheric motion vector observations (VARAMV) helped in shifting its peak rainfall toward Pune, IMD observatory by 18 km, though VARALL over-predicted rainfall by 60 mm than the observed. 相似文献
10.
Performance of a hybrid assimilation system combining 3D Var based NGFS (NCMRWF Global Forecast System) with ETR (Ensemble Transform with Rescaling) based Global Ensemble Forecast (GEFS) of resolution T-190L28 is investigated. The experiment is conducted for a period of one week in June 2013 and forecast skills over different spatial domains are compared with respect to mean analysis state. Rainfall forecast is verified over Indian region against combined observations of IMD and NCMRWF. Hybrid assimilation produced marginal improvements in overall forecast skill in comparison with 3D Var. Hybrid experiment made significant improvement in wind forecasts in all the regions on verification against mean analysis. The verification of forecasts with radiosonde observations also show improvement in wind forecasts with the hybrid assimilation. On verification against observations, hybrid experiment shows more improvement in temperature and wind forecasts at upper levels. Both hybrid and operational 3D Var failed in prediction of extreme rainfall event over Uttarakhand on 17 June, 2013. 相似文献
11.
BHASHA M MANKAD RASHMI SHARMA SUJIT BASU P K PAL 《Journal of Earth System Science》2012,121(1):251-262
Altimeter data have been assimilated in an ocean general circulation model using the water property conserving scheme. Two
runs of the model have been conducted for the year 2004. In one of the runs, altimeter data have been assimilated sequentially,
while in another run, assimilation has been suppressed. Assimilation has been restricted to the tropical Indian Ocean. An
assessment of the strength of the scheme has been carried out by comparing the sea surface temperature (SST), simulated in
the two runs, with in situ derived as well as remotely sensed observations of the same quantity. It has been found that the assimilation exhibits a
significant positive impact on the simulation of SST. The subsurface effect of the assimilation could be judged by comparing
the model simulated depth of the 20°C isotherm (hereafter referred to as D20), as a proxy of the thermocline depth, with the
same quantity estimated from ARGO observations. In this case also, the impact is noteworthy. Effect on the dynamics has been
judged by comparison of simulated surface current with observed current at a moored buoy location, and finally the impact
on model sea level forecast in a free run after assimilation has been quantified in a representative example. 相似文献
12.
P V Rajesh S Pattnaik D Rai K K Osuri U C Mohanty S Tripathy 《Journal of Earth System Science》2016,125(3):475-498
In 2013, Indian summer monsoon witnessed a very heavy rainfall event (>30 cm/day) over Uttarakhand in north India, claiming more than 5000 lives and property damage worth approximately 40 billion USD. This event was associated with the interaction of two synoptic systems, i.e., intensified subtropical westerly trough over north India and north-westward moving monsoon depression formed over the Bay of Bengal. The event had occurred over highly variable terrain and land surface characteristics. Although global models predicted the large scale event, they failed to predict realistic location, timing, amount, intensity and distribution of rainfall over the region. The goal of this study is to assess the impact of land state conditions in simulating this severe event using a high resolution mesoscale model. The land conditions such as multi-layer soil moisture and soil temperature fields were generated from High Resolution Land Data Assimilation (HRLDAS) modelling system. Two experiments were conducted namely, (1) CNTL (Control, without land data assimilation) and (2) LDAS, with land data assimilation (i.e., with HRLDAS-based soil moisture and temperature fields) using Weather Research and Forecasting (WRF) modelling system. Initial soil moisture correlation and root mean square error for LDAS is 0.73 and 0.05, whereas for CNTL it is 0.63 and 0.053 respectively, with a stronger heat low in LDAS. The differences in wind and moisture transport in LDAS favoured increased moisture transport from Arabian Sea through a convectively unstable region embedded within two low pressure centers over Arabian Sea and Bay of Bengal. The improvement in rainfall is significantly correlated to the persistent generation of potential vorticity (PV) in LDAS. Further, PV tendency analysis confirmed that the increased generation of PV is due to the enhanced horizontal PV advection component rather than the diabatic heating terms due to modified flow fields. These results suggest that, two different synoptic systems merged by the strong interaction of moving PV columns resulted in the strengthening and further amplification of the system over the region in LDAS. This study highlights the importance of better representation of the land surface fields for improved prediction of localized anomalous weather event over India. 相似文献
13.
While qualitative information from meteorological satellites has long been recognized as critical for monitoring weather events such as tropical cyclone activity, quantitative data are required to improve the numerical prediction of these events. In this paper, the sea surface winds from QuikSCAT, cloud motion vectors and water vapor winds from KALPANA-1 are assimilated using three-dimensional variational assimilation technique within Weather Research Forecasting (WRF) modeling system. Further, the sensitivity experiments are also carried out using the available cumulus convective parameterizations in WRF modeling system. The model performance is evaluated using available observations, and both qualitative and quantitative analyses are carried out while analyzing the surface and upper-air characteristics over Mumbai (previously Bombay) and Goa during the occurrence of the tropical cyclone PHYAN at the west coast of Indian subcontinent. The model-predicted surface and upper-air characteristics show improvements in most of the situations with the use of the satellite-derived winds from QuikSCAT and KALPANA-1. Some of the model results are also found to be better in sensitivity experiments using cumulus convection schemes as compared to the CONTROL simulation. 相似文献
14.
Rupa Kamineni S. R. H. Rizvi S. C. Kar U. C. Mohanty R. K. Paliwal 《Journal of Earth System Science》2002,111(3):351-364
Oceansat-1 was successfully launched by India in 1999, with two payloads, namely Multi-frequency Scanning Microwave Radiometer
(MSMR) and Ocean Color Monitor (OCM) to study the biological and physical parameters of the ocean. The MSMR sensor is configured
as an eight-channel radiometer using four frequencies with dual polarization. The MSMR data at 75 km resolution from the Oceansat-I
have been assimilated in the National Centre for Medium Range Weather Forecasting (NCMRWF) data assimilation forecast system.
The operational analysis and forecast system at NCMRWF is based on a T80L18 global spectral model and Spectral Statistical
Interpolation (SSI) scheme for data analysis. The impact of the MSMR data is seen globally, however it is significant over
the oceanic region where conventional data are rare. The dry-nature of the control analyses have been removed by utilizing
the MSMR data. Therefore, the total precipitable water data from MSMR has been identified as a very crucial parameter in this
study. The impact of surface wind speed from MSMR is to increase easterlies over the tropical Indian Ocean. Shifting of the
positions of westerly troughs and ridges in the south Indian Ocean has contributed to reduction of temperature to around 30‡S. 相似文献
15.
Paula Etala 《Natural Hazards》2009,51(1):79-95
The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds
was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting
practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected
a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast
range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h.
The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The
24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades
after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la
Plata area and extends to the first 3 days at the intermediate navigation channels. 相似文献
16.
V S PRASAD SAJI MOHANDAS SURYA KANTI DUTTA M DAS GUPTA G R IYENGAR E N RAJAGOPAL SWATI BASU 《Journal of Earth System Science》2014,123(2):247-258
Medium range weather forecasts are being generated in real time using Global Data Assimilation Forecasting System (GDAFS) at NCMRWF since 1994. The system has been continuously upgraded in terms of data usage, assimilation and forecasting system. Recently this system was upgraded to a horizontal resolution of T574 (about 22 km) with 64 levels in vertical. The assimilation scheme of this upgraded system is based on the latest Grid Statistical Interpolation (GSI) scheme and it has the provision to use most of available meteorological and oceanographic satellite datasets besides conventional meteorological observations. The new system has an improved procedure for relocating tropical cyclone to its observed position with the correct intensity. All these modifications have resulted in improvement of skill of medium range forecasts by about 1 day. 相似文献
17.
Paula Etala 《Natural Hazards》2009,51(1):49-61
The ability of the SMARA storm surge numerical prediction system to reproduce local effects in estuarine and coastal winds was recently improved by considering one-way coupling of the air–sea momentum exchange through the wave stress, and best forecasting practices for downscaling. The inclusion of long period atmospheric pressure forcing in tide and tide/surge calculations corrected a systematic error in the surge, produced by the South Atlantic Ocean quasi-stationary pressure patterns. The maximum forecast range for the storm surge at Buenos Aires provided by the real-time use of water level observations is approximately 12 h. The best available water level prediction is the 6-h forecast (nowcast) based on the closest water level observations. The 24-h forecast from the numerical models slightly improves this nowcast. Although the numerical forecast accuracy degrades after the first 48 h, the improvement to the full range observation-based prediction is maintained at the inner Río de la Plata area and extends to the first 3 days at the intermediate navigation channels. 相似文献
18.
Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India Meteorological Department 总被引:2,自引:2,他引:0
India Meteorological Department (IMD) introduced the objective tropical cyclone (TC) intensity forecast valid for next 24 h over the north Indian Ocean (NIO) in 2003 and extended up to 72 h in 2009. In this study, an attempt is made to evaluate the TC intensity forecast issued by IMD during 2005–2011 (7 years) by calculating the absolute error (AE), root mean square error (RMSE) and skill in intensity forecast in terms of maximum sustained surface wind (MSW). The accuracy of TC intensity forecast has been analysed with respect to basin of formation (Bay of Bengal, Arabian Sea and NIO as whole), season of formation (pre-monsoon and post-monsoon seasons), intensity of TCs (cyclonic storm and severe cyclonic storm or higher intensities) and type of track of TCs (climatological/straight moving and recurving/looping type). The study shows that the average AE (RMSE) in intensity forecast is about 11(14), 14(19) and 20(26) knots, respectively, for 24-, 48- and 72-h forecasts over the NIO as a whole during 2009–2011. The skill of intensity forecast is about 44 %(48 %), 60 %(58 %) and 60 %(65 %) for 24-, 48- and 72-h forecasts during 2009–2011 with respect to AE (RMSE). There is no significant improvement in terms of reduction in AE and RMSE of MSW forecast over the NIO like that over the northwest Pacific and northern Atlantic Oceans during 2005–2011. However, the skill in intensity forecast compared to persistence method has significantly improved by about 6 %(10 %) and 9 %(8 %) per year, respectively, for 12- and 24-h forecasts considering the AE (RMSE) during 2005–2011. There is also significant increasing trend in percentage of 24-h intensity forecasts with error of 10 knots or less during 2005–2011. 相似文献
19.
Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects 总被引:1,自引:0,他引:1
A K MITRA G R IYENGAR V R DURAI J SANJAY T N KRISHNAMURTI A MISHRA D R SIKKA 《Journal of Earth System Science》2011,120(1):27-52
Realistic simulation/prediction of the Asian summer monsoon rainfall on various space–time scales is a challenging scientific
task. Compared to mid-latitudes, a proportional skill improvement in the prediction of monsoon rainfall in the medium range
has not happened in recent years. Global models and data assimilation techniques are being improved for monsoon/tropics. However,
multi-model ensemble (MME) forecasting is gaining popularity, as it has the potential to provide more information for practical
forecasting in terms of making a consensus forecast and handling model uncertainties. As major centers are exchanging model
output in near real-time, MME is a viable inexpensive way of enhancing the forecasting skill and information content. During
monsoon 2008, on an experimental basis, an MME forecasting of large-scale monsoon precipitation in the medium range was carried
out in real-time at National Centre for Medium Range Weather Forecasting (NCMRWF), India. Simple ensemble mean (EMN) giving
equal weight to member models, bias-corrected ensemble mean (BCEMn) and MME forecast, where different weights are given to
member models, are the products of the algorithm tested here. In general, the aforementioned products from the multi-model
ensemble forecast system have a higher skill than individual model forecasts. The skill score for the Indian domain and other
sub-regions indicates that the BCEMn produces the best result, compared to EMN and MME. Giving weights to different models
to obtain an MME product helps to improve individual member models only marginally. It is noted that for higher rainfall values,
the skill of the global model rainfall forecast decreases rapidly beyond day-3, and hence for day-4 and day-5, the MME products
could not bring much improvement over member models. However, up to day-3, the MME products were always better than individual
member models. 相似文献
20.
One very specific operational requirement of the Tropical Cyclone (TC) Programme of the Regional Specialized Meteorological
Centre, New Delhi is to provide 12-hourly forecasts valid up to 48 h (preferably 72 h) on the intensity of cyclones over the
southern Indian Seas. In this paper, a simple empirical model for predicting the intensity of TCs occurring in the Bay of
Bengal is proposed. The model parameter has been determined from a database assembled on 30 recent cyclones, and the model
itself is based on the assumption that a TC intensifies exponentially. A method for correcting the forecast during subsequent
observation hours (6- or 12-h intervals) is also presented. The results show that the forecast skill for forecasts of up to
48 h is reasonably good. The absolute mean errors are less than 12 knots for 48-h forecasts, with the forecast skill decreasing
with time. With the incorporation of a correction procedure based on the latest observations, some improvement in the forecast
skill can be obtained. The model is expected to be useful to operational forecasters. 相似文献