共查询到20条相似文献，搜索用时 31 毫秒
1.
Sensitivity of MM5 and WRF mesoscale model predictions of surface winds in a typhoon to planetary boundary layer parameterizations 总被引：2，自引：1，他引：1
Sea surface winds and coastal winds, which have a significant influence on the ocean environment, are very difficult to predict.
Although most planetary boundary layer (PBL) parameterizations have demonstrated the capability to represent many meteorological
phenomena, little attention has been paid to the precise prediction of winds at the lowest PBL level. In this study, the ability
to simulate sea winds of two widely used mesoscale models, fifth-generation mesoscale model (MM5) and weather research and
forecasting model (WRF), were compared. In addition, PBL sensitivity experiments were performed using Medium-Range Forecasts
(MRF), Eta, Blackadar, Yonsei University (YSU), and Mellor–Yamada–Janjic (MYJ) during Typhoon Ewiniar in 2006 to investigate
the optimal PBL parameterizations for predicting sea winds accurately. The horizontal distributions of winds were analyzed
to discover the spatial features. The time-series analysis of wind speed from five sensitivity experimental cases was compared
by correlation analysis with surface observations. For the verification of sea surface winds, QuikSCAT satellite 10-m daily
mean wind data were used in root-mean-square error (RMSE) and bias error (BE) analysis. The MRF PBL using MM5 produced relatively
smaller wind speeds, whereas YSU and MYJ using WRF produced relatively greater wind speeds. The hourly surface observations
revealed increasingly strong winds after 0300 UTC, July 10, with most of the experiments reproducing observations reliably.
YSU and MYJ using WRF showed the best agreements with observations. However, MRF using MM5 demonstrated underestimated winds.
The conclusions from the correlation analysis and the RMSE and BE analysis were compatible with the above-mentioned results.
However, some shortcomings were identified in the improvements of wind prediction. The data assimilation of topographical
data and asynoptic observations along coast lines and satellite data in sparsely observed ocean areas should make it possible
to improve the accuracy of sea surface wind predictions. 相似文献
2.
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
3.
Heavy off-season rains in the tropics pose significant natural hazards largely because they are unexpected and the popular
infrastructure is ill-prepared. One such event was observed from January 9 to 11, 2002 in Senegal (14.00° N, 14.00°␣W), West
Africa. This tropical country is characterized by a long dry season from November to April or May. During this period, although
the rain-bearing monsoonal flow does not reach Senegal, the region can occasionally experience off-season rains. We conducted
a numerical simulation of the January 9–11, 2002 heavy off-season rain using the Fifth-Generation NCAR/Pennsylvania State
University Mesoscale Model (MM5) and the Weather Research and Forecasting (WRF) model. The objective was to delineate the
meteorological set-up that led to the heavy rains and flooding. A secondary objective was to test the model’s performance
in Senegal using relatively simpler (default) model configurations and local/regional observations. The model simulations
for both MM5 and WRF agree satisfactorily with the observations, particularly as regards the wind patterns, the intensification
of the rainfall, and the associated drop in temperatures. This situation provided the environment for heavy rainfall accompanied
by a cold wave. The results suggest that off-the-shelf weather forecast models can be applied with relatively simple physical
options and modest computational resources to simulate local impacts of severe weather episodes. In addition, these models
could become part of regional hazard mitigation planning and infrastructure. 相似文献
4.
The present study describes an analysis of Asian summer monsoon forecasts with an operational general circulation model (GCM)
of the European Centre for Medium Range Weather Forecasts (ECMWF), U.K. An attempt is made to examine the influence of improved
treatment of physical processes on the reduction of systematic errors. As some of the major changes in the parameterization
of physical processes, such as modification to the infrared radiation scheme, deep cumulus convection scheme, introduction
of the shallow convection scheme etc., were introduced during 1985–88, a thorough systematic error analysis of the ECMWF monsoon
forecasts is carried out for a period prior to the incorporation of such changes i.e. summer monsoon season (June–August)
of 1984, and for the corresponding period after relevant changes were implemented (summer monsoon season of 1988).
Monsoon forecasts of the ECMWF demonstrate an increasing trend of forecast skill after the implementation of the major changes
in parameterizations of radiation, convection and land-surface processes. Further, the upper level flow is found to be more
predictable than that of the lower level and wind forecasts display a better skill than temperature. Apart from this, a notable
increase in the magnitudes of persistence error statistics indicates that the monsoon circulation in the analysed fields became
more intense with the introduction of changes in the operational forecasting system.
Although, considerable reduction in systematic errors of the Asian summer monsoon forecasts is observed (up to day-5) with
the introduction of major changes in the treatment of physical processes, the nature of errors remain unchanged (by day-10).
The forecast errors of temperature and moisture in the middle troposphere are also reduced due to the changes in treatment
of longwave radiation. Moreover, the introduction of shallow convection helped it further by enhancing the vertical transports
of heat and moisture from the lower troposphere. Though, the hydrological cycle in the operational forecasts appears to have
enhanced with the major modifications and improvements to the physical parameterization schemes, certain regional peculiarities
have developed in the simulated rainfall distribution over the monsoon region. Hence, this study suggests further attempts
to improve the formulations of physical processes for further reduction of systematic forecast errors. 相似文献
5.
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
6.
Surya K. Dutta Someshwar Das S. C. Kar U. C. Mohanty P. C. Joshi 《Journal of Earth System Science》2009,118(5):413-440
The change in the type of vegetation fraction can induce major changes in the local effects such as local evaporation, surface
radiation, etc., that in turn induces changes in the model simulated outputs. The present study deals with the effects of
vegetation in climate modeling over the Indian region using the MM5 mesoscale model. The main objective of the present study
is to investigate the impact of vegetation dataset derived from SPOT satellite by ISRO (Indian Space Research Organization)
versus that of USGS (United States Geological Survey) vegetation dataset on the simulation of the Indian summer monsoon. The present
study has been conducted for five monsoon seasons (1998–2002), giving emphasis over the two contrasting southwest monsoon
seasons of 1998 (normal) and 2002 (deficient).
The study reveals mixed results on the impact of vegetation datasets generated by ISRO and USGS on the simulations of the
monsoon. Results indicate that the ISRO data has a positive impact on the simulations of the monsoon over northeastern India
and along the western coast. The MM5-USGS has greater tendency of overestimation of rainfall. It has higher standard deviation
indicating that it induces a dispersive effect on the rainfall simulation. Among the five years of study, it is seen that
the RMSE of July and JJAS (June–July–August–September) for All India Rainfall is mostly lower for MM5-ISRO. Also, the bias
of July and JJAS rainfall is mostly closer to unity for MM5-ISRO. The wind fields at 850 hPa and 200 hPa are also better simulated
by MM5 using ISRO vegetation. The synoptic features like Somali jet and Tibetan anticyclone are simulated closer to the verification
analysis by ISRO vegetation. The 2 m air temperature is also better simulated by ISRO vegetation over the northeastern India,
showing greater spatial variability over the region. However, the JJAS total rainfall over north India and Deccan coast is
better simulated using the USGS vegetation. Sensible heat flux over north-west India is also better simulated by MM5-USGS. 相似文献
7.
Ki-Young Heo Jeong-Wook Lee Kyung-Ja Ha Ki-Cheon Jun Kwang-Soon Park Jae-Il Kwon 《Natural Hazards》2009,51(1):151-162
High-quality informations on sea level pressure and sea surface wind stress are required to accurately predict storm surges
over the Korean Peninsula. The storm surge on 31 March 2007 at Yeonggwang, on the western coast, was an abrupt response to
mesocyclone development. In the present study, we attempted to obtain reliable surface winds and sea level pressures. Using
an optimal physical parameterization for wind conditions, MM5, WRF and COAMPS were used to simulate the atmospheric states
that accompanied the storm surge. The use of MM5, WRF and COAMPS simulations indicated the development of high winds in the
strong pressure gradient due to an anticyclone and a mesocyclone in the southern part of the western coast. The response to
this situation to the storm surge was sensitive. A low-level warm advection was examined as a possible causal mechanism for
the development of a mesocyclone in the generating storm surge. The low-level warm temperature advection was simulated using
the three models, but MM5 and WRF tended to underestimate the warm tongue and overestimate the wind speed. The WRF simulation
was closer to the observed data than the other simulations in terms of wind speed and the intensity of the mesocyclone. It
can be concluded that the magnitude of the storm surge at Yeonggwang was dependent, not only on the development of a mesocyclone
but on ocean effects as well. 相似文献
8.
分析研究了2001年5月15日～8月15日3个月GMS卫星资料在湖南资水流域实时数值预报中的应用以及将TRMM(Tropical Rainfall Measuring Mission)卫星上的TMI(Microwave Imager)雨水资料适时融入数值模式改变当时模式中雨水分布场,数值模拟还研究了发生在淮河流域的10次暴雨过程。结果表明:资水流域3个月的实时预报效果良好,准确预报出其中出现的3次致洪暴雨和1次特大暴雨;对淮河流域暴雨,由于TMI资料空间分辨率较高,能够很好地反映中小尺度系统的空间结构,加入模式后使得模拟出来的降雨强度,雨量中心时空分布更接近实际情况,10次暴雨过程的TS评分较不使用TMI资料更好。 相似文献
9.
A primitive equation spectral model has been successfully integrated for five days starting with the initial data of 26 February
1982. The geopotential heights and wind strengths are well predicted up to day 3. The 24-hour accumulated precipitation of
the model reasonably agrees only up to two days, with the observed rainfall under the influence of western disturbance that
prevailed over Afghanistan, Pakistan and north India till 3 March 1982. For one day global integration of the model, the CPU
time requirement in Cyber 170/730 is about one hour. 相似文献
10.
Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent 总被引：1，自引：0，他引：1
The three dimensional variational data assimilation scheme (3D-Var) is employed in the recently developed Weather Research
and Forecasting (WRF) model. Assimilation experiments have been conducted to assess the impact of Indian Space Research Organisation’s
(ISRO) Automatic Weather Stations (AWS) surface observations (temperature and moisture) on the short range forecast over the
Indian region. In this study, two experiments, CNT (without AWS observations) and EXP (with AWS observations) were made for
24-h forecast starting daily at 0000 UTC during July 2008. The impact of assimilation of AWS surface observations were assessed
in comparison to the CNT experiment. The spatial distribution of the improvement parameter for temperature, relative humidity
and wind speed from one month assimilation experiments demonstrated that for 24-h forecast, AWS observations provide valuable
information. Assimilation of AWS observed temperature and relative humidity improved the analysis as well as 24-h forecast.
The rainfall prediction has been improved due to the assimilation of AWS data, with the largest improvement seen over the
Western Ghat and eastern India. 相似文献
11.
The objective of this study is to investigate the impact of a surface data assimilation (SDA) technique, together with the
traditional four-dimensional data assimilation (FDDA), on the simulation of a monsoon depression that formed over India during
the field phase of the 1999 Bay of Bengal Monsoon Experiment (BOBMEX). The SDA uses the analyzed surface data to continuously
assimilate the surface layer temperature as well as the water vapor mixing ratio in the mesoscale model. The depression for
the greater part of this study was offshore and since successful application of the SDA would require surface information,
a method of estimating surface temperature and surface humidity using NOAA-TOVS satellites was used. Three sets of numerical
experiments were performed using a coupled mesoscale model. The first set, called CONTROL, uses the NCEP (National Center
for Environmental Prediction) reanalysis for the initial and lateral boundary conditions in the MM5 simulation. The second
and the third sets implemented the SDA of temperature and moisture together with the traditional FDDA scheme available in
the MM5 model. The second set of MM5 simulation implemented the SDA scheme only over the land areas, and the third set extended
the SDA technique over land as well as sea. Both the second and third sets of the MM5 simulation used the NOAA-TOVS and QuikSCAT
satellite and conventional upper air and surface meteorological data to provide an improved analysis. The results of the three
sets of MM5 simulations are compared with one another and with the analysis and the BOBMEX 1999 buoy, ship, and radiosonde
observations. The predicted sea level pressure of both the model runs with assimilation resembles the analysis closely and
also captures the large-scale structure of the monsoon depression well. The central sea level pressures of the depression
for both the model runs with assimilation were 2–4 hPa lower than the CONTROL. The results of both the model runs with assimilation
indicate a larger spatial area as well as increased rainfall amounts over the coastal regions after landfall compared with
the CONTROL. The impact of FDDA and SDA, the latter over land, resulted in reduced errors of the following: 1.45 K in temperature,
0.39 m s−1 in wind speed, and 14° in wind direction compared with the BOBMEX buoy observation, and 1.43 m s−1 in wind speed, 43° in wind direction, and 0.75% in relative humidity compared with the CONTROL. The impact of SDA over land
and sea compared with SDA over land only showed a further marginal reduction of errors: 0.23 K in air temperature (BOBMEX
buoy) and 1.33 m s−1 in wind speed simulations. 相似文献
12.
利用WRF3D-Var同化多普勒雷达反演风场试验研究 总被引：2，自引：0，他引：2
为了将C波段雷达风场资料更好地应用于数值预报模式中,利用两步变分法反演多普勒雷达风场资料,并处理成标准的常规探空资料,以WRF模式及其三维变分同化系统为平台,针对2013年6月19日发生在天水的一次强暴雨过程进行同化雷达反演风的试验研究.试验结果表明:同化雷达反演风场后,对降水预报的改进能维持12h,尤其同化雷达反演风场后3~9h效果非常显著;0~3h作用不是很明显;9~12h预报具有一定的正作用.另外,循环同化比同化一次效果好,但并不是同化次数越多越好.因此,同化C波段雷达反演风场后,对降水预报具有一定的正作用. 相似文献
13.
Statistical bias correction methods for numerical weather prediction (NWP) forecasts of maximum and minimum temperatures over India in the medium-range time scale (up to 5 days) are proposed in this study. The objective of bias correction is to minimize the systematic error of the next forecast using bias from past errors. The need for bias corrections arises from the many sources of systematic errors in NWP modeling systems. NWP models have shortcomings in the physical parameterization of weather events and have the inability to handle sub-grid phenomena successfully. The statistical algorithms used for minimizing the bias of the next forecast are running-mean (RM) bias correction, best easy systematic estimator, simple linear regression and the nearest neighborhood (NN) weighted mean, as they are suitable for small samples. Bias correction is done for four global NWP model maximum and minimum temperature forecasts. The magnitude of the bias at a grid point depends upon geographical location and season. Validation of the bias correction methodology is carried out using daily observed and bias-corrected model maximum and minimum temperature forecast over India during July–September 2011. The bias-corrected NWP model forecast generally outperforms direct model output (DMO). The spatial distribution of mean absolute error and root-mean squared error for bias-corrected forecast over India indicate that both the RM and NN methods produce the best skill among other bias correction methods. The inter-comparison reveals that statistical bias correction methods improve the DMO forecast in terms of accuracy in forecast and have the potential for operational applications. 相似文献
14.
J C JOSHI TANKESHWAR KUMAR SUNITA SRIVASTAVA DIVYA SACHDEVA 《Journal of Earth System Science》2017,126(1):3
Maximum and minimum temperatures are used in avalanche forecasting models for snow avalanche hazard mitigation over Himalaya. The present work is a part of development of Hidden Markov Model (HMM) based avalanche forecasting system for Pir-Panjal and Great Himalayan mountain ranges of the Himalaya. In this work, HMMs have been developed for forecasting of maximum and minimum temperatures for Kanzalwan in Pir-Panjal range and Drass in Great Himalayan range with a lead time of two days. The HMMs have been developed using meteorological variables collected from these stations during the past 20 winters from 1992 to 2012. The meteorological variables have been used to define observations and states of the models and to compute model parameters (initial state, state transition and observation probabilities). The model parameters have been used in the Forward and the Viterbi algorithms to generate temperature forecasts. To improve the model forecasts, the model parameters have been optimised using Baum–Welch algorithm. The models have been compared with persistence forecast by root mean square errors (RMSE) analysis using independent data of two winters (2012–13, 2013–14). The HMM for maximum temperature has shown a 4–12% and 17–19% improvement in the forecast over persistence forecast, for day-1 and day-2, respectively. For minimum temperature, it has shown 6–38% and 5–12% improvement for day-1 and day-2, respectively. 相似文献
15.
在全球变暖的背景下,南极已成为全球气候变化研究的热点,然而其区域内的观测站点稀疏且缺乏较长的时间序列,限制了人们对南极气候变化机制的分析与理解。Polar WRF作为目前最先进的极地区域气候模型之一,有力弥补了观测资料不足的缺陷,然而模式存在误差,在应用之前有必要对其定量评估。本文利用Polar WRF3.9.1对2004-2013年南极冰盖2m气温、10m风速和地表气压进行了数值模拟,并与28个气象站数据进行了对比分析,结果表明:模式对气温的模拟值在东南极沿岸偏低,在内陆偏高,在南极半岛既存在冷偏差也存在暖偏差,而对风速和气压的模拟整体呈高估。而从均方根误差和平均绝对误差的空间分布来看,模式对气温和气压的模拟结果在东南极沿岸的精度高于内陆和南极半岛,而风速则在内陆的精度要高于沿岸地区。但总体来说模拟效果较为理想,在2004-2013年间气温、风速、气压的模拟值的变化趋势与实测值的变化趋势相同。模式模拟的年平均2m气温和近地面气压在所有站点都通过了α=0.1的显著性检验,季节误差和月误差整体较小,且所有月份的相关系数都分别大于0.90与0.79。模式对10m风速的模拟精度要略低,部分沿岸站点的年平均误差超过了7.5m·s^(-1),但整体而言其在四季和各个月份的相关性均大于0.5且误差小于4.5m·s^(-1)。虽然Polar WRF作为天气模式,但在模拟长时间尺度的气候方面仍然表现较好。 相似文献
16.
Simulation of a flood producing rainfall event of 29 July 2010 over north-west Pakistan has been carried out using the Weather Research and Forecasting (WRF) model. This extraordinary rainfall event was localized over north-west Pakistan and recorded 274 mm of rainfall at Peshawar (34.02°N, 71.58°E), within a span of 24 h on that eventful day where monthly July normal rainfall is only 46.1 mm. The WRF model was run with the triple-nested domains of 27, 9, and 3 km horizontal resolution using Kain–Fritsch cumulus parameterization scheme having YSU planetary boundary layer. The model performance was evaluated by examining the different simulated parameters. The model-derived rainfall was compared with Pakistan Meteorological Department–observed rainfall. The model suggested that this flood producing heavy rainfall event over north-west region of Pakistan might be the result of an interaction of active monsoon flow with upper air westerly trough (mid-latitude). The north-west Pakistan was the meeting point of the southeasterly flow from the Bay of Bengal following monsoon trough and southwesterly flow from the Arabian Sea which helped to transport high magnitude of moisture. The vertical profile of the humidity showed that moisture content was reached up to upper troposphere during their mature stage (monsoon system usually did not extent up to that level) like a narrow vertical column where high amounts of rainfall were recorded. The other favourable conditions were strong vertical wind shear, low-level convergence and upper level divergence, and strong vorticity field which demarked the area of heavy rainfall. The WRF model might be able to simulate the flood producing rainfall event over north-west Pakistan and associated dynamical features reasonably well, though there were some spatial and temporal biases in the simulated rainfall pattern. 相似文献
17.
Matthew Simpson Sethu Raman R. Suresh U. C. Mohanty 《Journal of Earth System Science》2008,117(6):897-909
Doppler radar derived wind speed and direction profiles showed a well developed sea breeze circulation over the Chennai, India
region on 28 June, 2003. Rainfall totals in excess of 100 mm resulted from convection along the sea breeze front. Inland propagation
of the sea breeze front was observed in radar reflectivity imagery. High-resolution MM5 simulations were used to investigate
the influence of Chennai urban land use on sea breeze initiated convection and precipitation. A comparison of observed and
simulated 10m wind speed and direction over Chennai showed that the model was able to simulate the timing and strength of
the sea breeze. Urban effects are shown to increase the near surface air temperature over Chennai by 3.0K during the early
morning hours. The larger surface temperature gradient along the coast due to urban effects increased onshore flow by 4.0m
s−1. Model sensitivity study revealed that precipitation totals were enhanced by 25mm over a large region 150 km west of Chennai
due to urban effects. Deficiency in model physics related to night-time forecasts are addressed. 相似文献
18.
Vassiliki Kotroni Evangelos Floros Konstantinos Lagouvardos Goran Pejanovic Luka Ilic Momcilo Zivkovic 《Earth Science Informatics》2010,3(4):209-218
Weather forecasting is based on the use of numerical weather prediction (NWP) models that are able to perform the necessary
calculations that describe/predict the major atmospheric processes. One common problem in weather forecasting derives from
the uncertainty related to the chaotic behaviour of the atmosphere. A solution to that problem is to perform in addition to
“deterministic” forecasts, “stochastic” forecasts that provide an estimate of the prediction skill. A computationally feasible
approach towards this aim is to perform “ensemble forecasts”. Indeed, in the frame of SEE-GRID-SCI EU funded project a Regional
scale Multi-model, Multi-analysis ensemble forecasting system (REFS) was built and ported on the Grid infrastructure. REFS
is based on the use of four limited area models (namely BOLAM, MM5, ETA, and NMM) that are run using a multitude of initial
and boundary conditions over the Mediterranean. This paper presents the tools and procedures followed for developing this
application at a production level. 相似文献
19.
Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon 总被引：1，自引：0，他引：1
In the last thirty years great strides have been made by large-scale operational numerical weather prediction models towards
improving skills for the medium range time-scale of 7 days. This paper illustrates the use of these current forecasts towards
the construction of a consensus multimodel forecast product called the superensemble. This procedure utilizes 120 of the recent-past
forecasts from these models to arrive at the training phase statistics. These statistics are described by roughly 107 weights. Use of these weights provides the possibility for real-time medium range forecasts with the superensemble. We show
the recent status of this procedure towards real-time forecasts for the Asian summer monsoon. The member models of our suite
include ECMWF, NCEP/EMC, JMA, NOGAPS (US Navy), BMRC, RPN (Canada) and an FSU global spectral forecast model. We show in this
paper the skill scores for day 1 through day 6 of forecasts from standard variables such as winds, temperature, 500 hPa geopotential
height, sea level pressure and precipitation. In all cases we noted that the superensemble carries a higher skill compared
to each of the member models and their ensemble mean. The skill matrices we use include the RMS errors, the anomaly correlations
and equitable threat scores. For many of these forecasts the improvements of skill for the superensemble over the best model
was found to be quite substantial. This real-time product is being provided to many interested research groups. The FSU multimodel
superensemble, in real-time, stands out for providing the least errors among all of the operational large scale models. 相似文献
20.
Frequent occurrence of fog in different parts of northern India is common during the winter months of December and January. Low visibility conditions due to fog disrupt normal public life. Visibility conditions heavily affect both surface and air transport. A number of flights are either diverted or cancelled every year during the winter season due to low visibility conditions, experienced at different airports of north India. Thus, fog and visibility forecasts over plains of north India become very important during winter months. This study aims to understand the ability of a NWP model (NCMRWF, Unified Model, NCUM) with a diagnostic visibility scheme to forecast visibility over plains of north India. The present study verifies visibility forecasts obtained from NCUM against the INSAT-3D fog images and visibility observations from the METAR reports of different stations in the plains of north India. The study shows that the visibility forecast obtained from NCUM can provide reasonably good indication of the spatial extent of fog in advance of one day. The fog intensity is also predicted fairly well. The study also verifies the simple diagnostic model for fog which is driven by NWP model forecast of surface relative humidity and wind speed. The performance of NWP model forecast of visibility is found comparable to that from simple fog model driven by NWP forecast of relative humidity and wind speed. 相似文献