Impact of additional surface observation network on short range weather forecast during summer monsoon 2008 over Indian subcontinent

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.     

Analysis on MM5 predictions at Sriharikota during northeast monsoon 2008

The Indian northeast monsoon is inherently chaotic in nature as the rainfall realised in the peninsular India depends substantially on the formation and movement of low-pressure systems in central and southwest Bay of Bengal and on the convective activity which is mainly due to the moist north-easterlies from Bay of Bengal. The objective of this study is to analyse the performance of the PSU-NCAR Mesoscale Model Version 5 (MM5), for northeast monsoon 2008 that includes tropical cyclones – Rashmi, Khai-Muk and Nisha and convective events over Sriharikota region, the rocket launch centre. The impact of objective analysis system using radiosonde observations, surface observations and Kalpana-1 satellite derived Atmospheric Motion Wind Vectors (AMV) is also studied. The performance of the model is analysed by comparing the predicted parameters like mean sea level pressure (MSLP), intensity, track and rainfall with the observations. The results show that the model simulations could capture MSLP and intensity of all the cyclones reasonably well. The dependence of the movement of the system on the environmental flow is clearly observed in all the three cases. The vector displacement error and percentage of improvement is calculated to study the impact of objective data analysis on the movement and intensity of the cyclone.     

Performance evaluation of HWRF model during post monsoon tropical cyclone Nilofar

An accurate tropical cyclone track and intensity forecast is very important for disaster management. Specialized numerical prediction models have been recently used to provide high-resolution temporal and special forecasts. Hurricane Weather Research and Forecast (HWRF) model is one of the emerging numerical models for tropical cyclone forecasting. This study evaluates the performance of HWRF model during the post monsoon tropical cyclone Nilofar on the north Indian Ocean basin. The evaluation uses the best track data provided by the Indian Meteorological Department (IMD) and the Joint Typhoon Warning Centre (JTWC). Cyclone track, central pressure, and wind speed are covered on this evaluation. Generally, HWRF was able to predict the Nilofar track with track error less than 230 km within the first 66 h of forecast time span. HWRF predicted more intense tropical cyclone. It predicted the lowest central pressure to be 922 hPa while it reached 950 hPa according to IMD and 937 hPa according to JTWC. Wind forecast was better as it predicted maximum wind speed of 122 kt while it reached 110 and 115 kt according to IMD and JTWC, respectively.     

Experimental real-time multi-model ensemble (MME) prediction of rainfall during monsoon 2008: Large-scale medium-range aspects

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.     

Marine boundary layer simulation and verification during BOBMEX-Pilot using NCMRWF model

A global spectral model (T80L18) that is operational at NCMRWF is utilized to study the structure of the marine boundary layer over the Bay of Bengal during the BOBMEX-Pilot period. The vertical profiles of various meteorological parameters within the boundary layer are studied and verified against the available observations. The diurnal variation of various surface fields are also studied. The impact of non-local closure scheme for the boundary layer parameterisation is seen in simulation of the flow pattern as well as on the boundary layer structure over the oceanic region.     

Intensity forecast of tropical cyclones over North Indian Ocean using multilayer perceptron model: skill and performance verification

The coastal regions of India are profoundly affected by tropical cyclones during both pre- and post-monsoon seasons with enormous loss of life and property leading to natural disasters. The endeavour of the present study is to forecast the intensity of the tropical cyclones that prevail over Arabian Sea and Bay of Bengal of North Indian Ocean (NIO). A multilayer perceptron (MLP) model is developed for the purpose and compared the forecast through MLP model with other neural network and statistical models to assess the forecast skill and performances of MLP model. The central pressure, maximum sustained surface wind speed, pressure drop, total ozone column and sea surface temperature are taken to form the input matrix of the models. The target output is the intensity of the tropical cyclones as per the T??number. The result of the study reveals that the forecast error with MLP model is minimum (4.70?%) whereas the forecast error with radial basis function network (RBFN) is observed to be 14.62?%. The prediction with statistical multiple linear regression and ordinary linear regression are observed to be 9.15 and 9.8?%, respectively. The models provide the forecast beyond 72?h taking care of the change in intensity at every 3-h interval. The performance of MLP model is tested for severe and very severe cyclonic storms like Mala (2006), Sidr (2007), Nargis (2008), Aila (2009), Laila (2010) and Phet (2010). The forecast errors with MLP model for the said cyclones are also observed to be considerably less. Thus, MLP model in forecasting the intensity of tropical cyclones over NIOs may thus be considered to be an alternative of the conventional operational forecast models.     

Current status of multimodel superensemble and operational NWP forecast of the Indian summer monsoon

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.     

基于统计-动力反演的近千年印度夏季风驱动机制探讨

过去1000年的气候变化是最近数十年人类活动影响加强情况下全球气候变化的自然背景, 其变化规律和驱动机制的研究对预测未来气候变化有着重要意义。非线性统计-动力反演方法结合了统计模型和动力模型的优点, 能充分利用观测数据反演系统各因子之间的相互关系。本文尝试应用非线性统计-动力反演方法建立印度夏季风的动力方程, 为研究印度夏季风的驱动机制提供量化参考。经研究发现:近千年印度夏季风系统是复杂非线性动力系统; 工业革命前印度夏季风变化的主要驱动力是北大西洋海表温, 其次是温室气体(N2O和CO2)浓度与阿拉伯海海表温、ENSO及太阳辐照度等的相互作用; 在工业革命后期, 温室气体(CH4、N2O和CO2)浓度及其与北大西洋海表温、太阳辐照度、ENSO及北极温度等的相互作用成为印度夏季风的主要驱动力; 单因子甲烷和N2O是印度夏季风的驱动力, 而它们的非线性相互作用(两个因子的交叉项)却是稳定作用力。总体来说, 工业革命前, 北大西洋海表温度是印度夏季风的主要驱动因子; 工业革命后, 温室气体则成为主要的驱动因子。     

Mesoscale model simulation of low level equatorial winds over Borneo during the haze episode of September 1997

The large-scale vegetation fires instigated by the local farmers during the dry period of the major El Niño event in 1997 can be considered as one of the worst environmental disasters that have occurred in southeast Asia in recent history. This study investigated the local meteorology characteristics of an equatorial environment within a domain that includes the northwestern part of Borneo from the 17 to 27 September 1997 during the height of the haze episode by utilizing a limited area three-dimensional meteorological and dispersion model, The Air Pollution Model (TAPM).Daily land and sea breeze conditions near the northwestern coast of Borneo in the state of Sarawak, Malaysia were predicted with moderate success by the index of agreement of less than one between the observed and simulated values for wind speed and a slight overprediction of 2.3 of the skill indicator that evaluates the standard deviation to the observed values. The innermost domain of study comprises an area of 24,193 km², from approximately 109°E to 111°E, and from 1°N to 2.3°N, which includes a part of the South China Sea. Tracer analysis of air particles that were sourced in the state of Sarawak on the island of Borneo verified the existence of the landward and shoreward movements of the air during the simulation of the low level wind field. Polluted air particles were transported seawards during night-time, and landwards during daytime, highlighting the recirculation features of aged and newer air particles during the length of eleven days throughout the model simulation. Near calm conditions at low levels were simulated by the trajectory analysis from midnight to mid-day on the 22 of September 1997. Low-level turbulence within the planetary boundary layer in terms of the total kinetic energy was weak, congruent with the weak strength of low level winds that reduced the ability of the air to transport the pollutants.Statistical evaluation showed that parameters such as the systematic RMSE and unsystematic RMSE between the observed and simulated values indicated the modest skill of the model in simulating the low level winds. Otherwise, the equatorial meteorological parameters such as wind speed and temperature were successfully simulated by the model with comparatively high correlation coefficients, lower RMSEs and moderately high indices of agreement with observed values.     

Indian summer monsoon during the last two millennia

The monsoon is a large‐scale feature of the tropical atmospheric circulation, affecting people and economies in the world's most densely populated regions. Future trends due to natural variability and human‐induced climate changes are uncertain. Palaeoclimate records can improve our understanding of monsoon dynamics and thereby reduce this uncertainty. Palaeoclimate records have revealed a dramatic decrease in the Asian summer monsoon since the early Holocene maximum 9 ka BP. Here we focus on the last 2 ka, where some records indicate an increasing trend in the summer monsoon. Analysing Globigerina bulloides upwelling records from the Arabian Sea, we find the weakest monsoon occurred 1500 a BP, with an increasing trend towards the present. Copyright © 2010 John Wiley & Sons, Ltd.     

Asian monsoon climate during the Last Glacial Maximum: palaeo‐data–model comparisons

The Last Glacial Maximum (LGM) (23–19 ka BP) in the Asian monsoon region is generally described as cool and dry, due to a strong winter monsoon. More recently, however, palaeo‐data and climate model simulations have argued for a more variable LGM Asian monsoon climate with distinct regional differences. We compiled, evaluated, and partly re‐assessed proxy records for the Asian monsoon region in terms of wet/dry climatic conditions based on precipitation and effective moisture, and of sea surface temperatures. The comparison of the palaeo‐data set to LGM simulations by the Climate Community System Model version 3 (CCSM3) shows fairly good agreement: a dry LGM climate in the western and northern part due to a strengthened winter monsoon and/or strengthened westerly winds and wetter conditions in equatorial areas, due to a stronger summer monsoon. Data–model discrepancies are seen in some areas and are ascribed to the fairly coarse resolution of CCSM3 and/or to uncertainties in the reconstructions. Differences are also observed between the reconstructed and simulated northern boundaries of the Intertropical Convergence Zone (ITCZ). The reconstructions estimate a more southern position over southern India and the Bay of Bengal, whereas CCSM3 simulates a more northern position. In Indochina, the opposite is the case. The palaeo‐data indicate that climatic conditions changed around 20–19 ka BP, with some regions receiving higher precipitation and some experiencing drier conditions, which would imply a distinct shift in summer monsoon intensity. This shift was probably triggered by the late LGM sea‐level rise, which led to changes in atmosphere–ocean interactions in the Indian Ocean. The overall good correspondence between reconstructions and CCSM3 suggests that CCSM3 simulates LGM climate conditions over subtropical and tropical areas fairly well. The few high‐resolution qualitative and quantitative palaeo‐records available for the large Asian monsoon region make reconstructions however still uncertain.     

Meteorological fields variability over the Indian seas in pre and summer monsoon months during extreme monsoon seasons

In this study, the possible linkage between summer monsoon rainfall over India and surface meteorological fields (basic fields and heat budget components) over monsoon region (30‡E-120‡E, 30‡S30‡N) during the pre-monsoon month of May and summer monsoon season (June to September) are examined. For this purpose, monthly surface meteorological fields anomaly are analyzed for 42 years (1958-1999) using reanalysis data of NCEP/NCAR (National Center for Environmental Prediction/National Center for Atmospheric Research). The statistical significance of the anomaly (difference) between the surplus and deficient monsoon years in the surface meteorological fields are also examined by Student’s t-test at 95% confidence level. Significant negative anomalies of mean sea level pressure are observed over India, Arabian Sea and Arabian Peninsular in the pre-monsoon month of May and monsoon season. Significant positive anomalies in the zonal and meridional wind (at 2 m) in the month of May are observed in the west Arabian Sea off Somali coast and for monsoon season it is in the central Arabian Sea that extends up to Somalia. Significant positive anomalies of the surface temperature and air temperature (at 2 m) in the month of May are observed over north India and adjoining Pakistan and Afghanistan region. During monsoon season this region is replaced by significant negative anomalies. In the month of May, significant positive anomalies of cloud amount are observed over Somali coast, north Bay of Bengal and adjoining West Bengal and Bangladesh. During monsoon season, cloud amount shows positive anomalies over NW India and north Arabian Sea. There is overall reduction in the incoming shortwave radiation flux during surplus monsoon years. A higher magnitude of latent heat flux is also found in surplus monsoon years for the month of May as well as the monsoon season. The significant positive anomaly of latent heat flux in May, observed over southwest Arabian Sea, may be considered as an advance indicator of the possible behavior of the subsequent monsoon season. The distribution of net heat flux is predominantly negative over eastern Arabian Sea, Bay of Bengal and Indian Ocean. Anomaly between the two extreme monsoon years in post 1980 (i.e., 1988 and 1987) shows that shortwave flux, latent heat flux and net heat flux indicate reversal in sign, particularly in south Indian Ocean. Variations of the heat budget components over four smaller sectors of Indian seas, namely Arabian Sea, Bay of Bengal and west Indian Ocean and east Indian Ocean show that a small sector of Arabian Sea is most dominant during May and other sectors showing reversal in sign of latent heat flux during monsoon season.     

Variations in sulphate aerosols concentration during winter monsoon season for two consecutive years using a general circulation model

During the field cruises of the Indian Ocean Experiment (INDOEX) extensive measurements on the atmospheric chemical and aerosol composition are undertaken to study the long-range transport of air pollution from south and southeast Asia towards the Indian Ocean during the dry monsoon season in 1998 and 1999. The present paper discusses the temporal and spatial variations in aerosols and aerosol forcing during the winter monsoon season (January-March) for INDOEX first field phase (FFP) in 1998 and INDOEX intensive field phase (IFP) in 1999. An interactive chemistry/aerosol model (LMDZ.3.3) is used to investigate the variation in the spatial distribution of tropospheric sulphate aerosols during 1998 and 1999. The model results depict major enhancement in the sulphate aerosol concentrations, radiative forcing (RF) and optical depth over the Indian subcontinent and adjoining marine areas between INDOEX-FFP and IFP. A significant increase in transport of sulphate aerosols from the continents to the Indian Ocean region has also been simulated during the winter monsoon in 1999. The mean RF over INDOEX-FFP in 1998 is found to be ?1.2 Wm^–2 while it increased to ?1.85 Wm^–2 during INDOEX-IFP in 1999. Model results reveal a mean sulphate aerosol optical depth (AOD) of 0.08 and 0.14 over Indian subcontinent during 1998 and 1999, respectively. The model results suggest that elevated AOD downwind of source regions in India can significantly affect the regional air quality and adjoining marine environments.     

河口海域SDZY水质预测模型的研究及应用

根据弱混合型河口海域的水流条件及边界条件,针对温热排水污染源,从物理机制入手,提出了三维 多层二维平面综合预测模型,对排污口区域,用三维模型考虑了垂直方向的流动及流场的梯度变化,同时考虑了垂直方向的热量交换和温度场的梯度变化,对远离排污口区域,用多层平面二维模型,考虑各层的流速场和温度场的梯度变化。通过对实际河口海域流场和温度场的模拟计算,模型具有较高的精度和实用价值。     

Hydrography of the eastern Arabian Sea during summer monsoon 2002

Hydrographic observations in the eastern Arabian Sea (EAS) during summer monsoon 2002 (during the first phase of the Arabian Sea Monsoon Experiment (ARMEX)) include two approximately fortnight-long CTD time series. A barrier layer was observed occasionally during the two time series. These ephemeral barrier layers were caused byin situ rainfall, and by advection of low-salinity (high-salinity) waters at the surface (below the surface mixed layer). These barrier layers were advected away from the source region by the West India Coastal Current and had no discernible effect on the sea surface temperature. The three high-salinity water masses, the Arabian Sea High Salinity Water (ASHSW), Persian Gulf Water (PGW), and Red Sea Water (RSW), and the Arabian Sea Salinity Minimum also exhibited intermittency: they appeared and disappeared during the time series. The concentration of the ASHSW, PGW, and RSW decreased equatorward, and that of the RSW also decreased offshore. The observations suggest that the RSW is advected equatorward along the continental slope off the Indian west coast.     

The value ofC efor the Arabian Sea during summer monsoon

We estimate, from the moisture budget the bulk aerodynamic coefficient for latent heat flux (C _e) during the monsoon season over the central Arabian Sea. The average value ofC _eunder active monsoon conditions was found to be 2.25 × 10⁻³ which is nearly 60% higher than those previously used.     

区域地下水位预报的季节型神经网络模型

根据西北干旱区地下水位周期性变化趋势明显的特点,提出了测井水位预报的季节型神经网络模型。同时,针对系列不同周期内变幅各异的特点,对监测序列进行了光滑处理。预报结果表明:该模型预测效果较好,运行合理,对规律性不强的地下水位动态预报具有一定的实用价值。     

近千年东亚夏季风演变*

在分析东亚地区夏季海平面气压场与中国东部6区域干湿指数关系的基础上,重建了公元960—2000年近千年东亚地区夏季海平面气压场的格点资料,并对重建效果进行了检验。同时,依据重建的海平面气压场资料定义了近千年东亚夏季风强度指数,探讨了近千年东亚夏季风的变化特征。结果表明：（1）重建的近千年东亚地区的海平面气压场具有一定的可信度,它为研究更长时间尺度的东亚夏季风变化特征提供了基础;（2）东亚夏季风指数存在60～70 a、30～40 a、10～20 a的显著周期变化;（3）近千年东亚夏季风的强度指数主要经历过9次明显的趋势突变。其中,13世纪30年代东亚夏季风的强度指数经历了最显著的振动。     

Multivariate forecast of winter monsoon rainfall in India using SST anomaly as a predictor: Neurocomputing and statistical approaches

In this article, the complexities in the relationship between rainfall and sea surface temperature (SST) anomalies during the winter monsoon over India were evaluated statistically using scatter plot matrices and autocorrelation functions. Linear, as well as polynomial trend equations were obtained, and it was observed that the coefficient of determination for the linear trend was very low and it remained low even when polynomial trend of degree six was used. An exponential regression equation and an artificial neural network with extensive variable selection were generated to forecast the average winter monsoon rainfall of a given year using the rainfall amounts and the SST anomalies in the winter monsoon months of the previous year as predictors. The regression coefficients for the multiple exponential regression equation were generated using Levenberg-Marquardt algorithm. The artificial neural network was generated in the form of a multilayer perceptron with sigmoid non-linearity and genetic-algorithm based variable selection. Both of the predictive models were judged statistically using the Willmott's index, percentage error of prediction, and prediction yields. The statistical assessment revealed the potential of artificial neural network over exponential regression.     

冻融模型相似性分析及试验验证

为提高冻融模型与原型的相似程度,提出冻结指数相似比和实验室修正系数的概念及计算方法。以哈尔滨地区黄土为试验材料,将该地区2010–2012年3年日平均气温进行线性简化,基于含相变热传导的微分方程基础上,采用相似理论,在几何、温度和时间比尺约束下对密度为1.60 g/cm3、含水率为20 %的试样进行冻融循环模型试验,确定实验室修正系数K值为1.13,得到冻结指数相似比CI为1:102.5。对冻融阶段温控历时进行优化,进行冻融循环模型试验。通过对比可知,优化后模型的最大冻深值为21.33 cm,与模型预定值20 cm相差仅6.5 %;优化后冻融模型弥补了太阳辐射对冻深的影响,一个冻融周期由108.14 h减少为84.53 h。以实验室修正系数K和冻结指数相似比CI优化冻融过程的各温控阶段历时合理,显著提高了冻融模型的最大冻深和冻融过程在几何比尺和时间比尺上的相似度。