Optimisation of Hidden Markov Model using Baum–Welch algorithm for prediction of maximum and minimum temperature over Indian Himalaya

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.     

Location specific forecasting of maximum and minimum temperatures over India by using the statistical bias corrected output of global forecasting system

The output from Global Forecasting System (GFS) T574L64 operational at India Meteorological Department (IMD), New Delhi is used for obtaining location specific quantitative forecast of maximum and minimum temperatures over India in the medium range time scale. In this study, a statistical bias correction algorithm has been introduced to reduce the systematic bias in the 24–120 hour GFS model location specific forecast of maximum and minimum temperatures for 98 selected synoptic stations, representing different geographical regions of India. The statistical bias correction algorithm used for minimizing the bias of the next forecast is Decaying Weighted Mean (DWM), as it is suitable for small samples. The main objective of this study is to evaluate the skill of Direct Model Output (DMO) and Bias Corrected (BC) GFS for location specific forecast of maximum and minimum temperatures over India. The performance skill of 24–120 hour DMO and BC forecast of GFS model is evaluated for all the 98 synoptic stations during summer (May-August 2012) and winter (November 2012–February 2013) seasons using different statistical evaluation skill measures. The magnitude of Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) for BC GFS forecast is lower than DMO during both summer and winter seasons. The BC GFS forecasts have higher skill score as compared to GFS DMO over most of the stations in all day-1 to day-5 forecasts during both summer and winter seasons. It is concluded from the study that the skill of GFS statistical BC forecast improves over the GFS DMO remarkably and hence can be used as an operational weather forecasting system for location specific forecast over India.     

Evaluation of operational tropical cyclone intensity forecasts over north Indian Ocean issued by India Meteorological Department

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.     

1961 - 2017年中国东北地区降雪时空演变特征分析

利用东北地区162个气象台站逐日降水量和天气现象数据, 采用统计分析方法, 对近57年（1961 - 2017年）降雪的气候特征和时空演变规律进行了分析。结果表明： 降雪量和降雪日数最多出现在12月, 小雪和中雪最多出现在11月或12月, 大雪和暴雪在冬末春初出现概率最高。降雪分布为山地大于平原, 平原地区自北向南、 自东向西减少, 降雪高值区主要位于大兴安岭北部、 小兴安岭和长白山区, 降雪强度中心位于长白山区和辽宁中部平原地区。年、 秋季、 冬季、 春季降雪量占同期降水量比例分别为4.7%、 7.0%、 84.4%和7.6%; 辽宁省西部山区和南部大连地区日最大降雪量占年总降雪量比例最高, 最长连续降雪日数在2 d以下, 降雪较高纬度地区更为集中。近57年降雪量和降雪强度分别以1.93 mm?（10a）^-1和0.11 mm?d^-1?（10a）^-1的速率显著增加, 降雪日数以2.08 d?（10a）^-1速率显著减少; 降雪量增加主要表现为各等级降雪量的增加, 降雪日数减少主要是微量和小雪日数的减少, 降雪强度增加主要为大雪和暴雪降雪强度的增加。年、 秋季和冬季降雪量占同期降水量比例平均每10年增加0.36%、 0.48%和0.45%, 春季以0.11%?（10a）^-1的速率减少。中雪、 大雪和暴雪对降雪贡献率均呈增加趋势, 小雪降雪量和微量降雪日数贡献率减少; 1987年降雪量和降雪日数突变后, 微量降雪日数和暴雪日数、 小雪降雪量贡献率改变显著。就区域平均而言, 2001 - 2017年的降雪量较1961 - 1980年增加了27.8%, 降雪日数减少了22.4%。     

1961-2017年青海高原降雪时空变化分析研究

基于1961-2018年青海高原47个台站观测资料,分析了青海高原降雪量、降雪日数的时空演变特征,结果表明：青海高原地区降雪量呈明显的减少趋势,每10年减少3.7 mm,其中1981-1989年、1990-1999年为降雪量偏多期,2000年以来为降雪量偏少期;近57年来青海高原降雪平均日数为11～43 d,青海高原降雪日数及各量级降雪日数总体均无明显趋势性变化,但存在阶段性变化;青海高原降雪量及降雪日数除常年干旱区柴达木盆地均为低值区外,其余地区高海拔地区多于低海拔地区,南部多于北部;青海高原月平均降雪量呈“U”型分布,而月平均降雪日数呈单峰型分布,降雪日数在冬季中末期偏多,春季偏少,其中小雪以上量级降雪日数易发生在秋末冬初,冬末向春季转换的时段内;近57年来青海高原降雪量在2002年前后存在明显的突变现象,其中青南牧区、青海湖地区及东部农业区年降雪量分别在2001年,1996年以及1996年前后存在明显突变现象,柴达木盆地降雪量无明显突变现象;而青海高原降雪日数在2000年前后存在明显突变现象,其中青南牧区1980年、2001年前后存在明显的突变现象,其余3个地区降雪日数无明显突变现象。     

1961-2015年我国鲁东南地区降雪的气候特征分析

利用鲁东南地区18个代表站1961-2015年的逐日降水量、逐日天气现象、积雪深度资料,对近55 a来降雪的气候特征进行了统计分析。结果表明：鲁东南地区年均降雪日数、强降雪日数、降雪量、强降雪量及年均雪深、年最大积雪深度的空间分布总体上山区多于平原和沿海,区域差异明显。21世纪00年代以前为多雪时期,以后为少雪时期。近55 a的年均降雪日数、强降雪日数、降雪量、强降雪量及年均雪深、年最大积雪深度皆呈减少趋势,降雪由多转少的转折年份均在1993年,年均雪深、年最大积雪深度的减少分别出现在1987年、1986年。鲁东南地区降雪主要集中在1-2月份,3月份强降雪量最大,平均雪深、最大积雪深度的最大月份分别出现在11月份、3月份。降雪时段为10月23日-次年4月28日,降雪的初终日西北部山区皆为最早。降雪日数、强降雪日数、降雪量、强降雪量、雪深均存在3 a的周期,最大积雪深度存在4～5 a的周期。     

Logistic models as a forecasting tool for snow avalanches in a cold maritime climate: northern Gaspésie,Québec,Canada

Snow avalanches are a major natural hazard for road users and infrastructure in northern Gaspésie. Over the past 11 years, the occurrence of nearly 500 snow avalanches on the two major roads servicing the area was reported. No management program is currently operational. In this study, we analyze the weather patterns promoting snow avalanche initiation and use logistic regression (LR) to calculate the probability of avalanche occurrence on a daily basis. We then test the best LR models over the 2012–2013 season in an operational forecasting perspective: Each day, the probability of occurrence (0–100%) determined by the model was classified into five classes avalanche danger scale. Our results show that avalanche occurrence along the coast is best predicted by 2 days of accrued snowfall [in water equivalent (WE)], daily rainfall, and wind speed. In the valley, the most significant predictive variables are 3 days of accrued snowfall (WE), daily rainfall, and the preceding 2 days of thermal amplitude. The large scree slopes located along the coast and exposed to strong winds tend to be more reactive to direct snow accumulation than the inner-valley slopes. Therefore, the probability of avalanche occurrence increases rapidly during a snowfall. The slopes located in the valley are less responsive to snow loading. The LR models developed prove to be an efficient tool to forecast days with high levels of snow avalanche activity. Finally, we discuss how road maintenance managers can use this forecasting tool to improve decision making and risk rendering on a daily basis.     

基于CMIP5的东亚地区降雪量变化特征分析

利用JMA的JRA-55降雪量及CMIP5的6个模式模拟的降雪量资料, 分析了东亚地区降雪量年变化特征及年际变化特征. 结果表明: 东亚地区降雪量在1958-2004年期间具有明显的年际变化特征及区域分布特征; 降雪主要集中在11月至翌年的4月, 这6个月中降雪量占年总降雪量的82%; 年际变化特征呈现出一种波动变化略有增加的趋势, 但是增加的幅度有所不同. 从区域分布特征来看, 东亚地区降雪主要分布在东北亚、青藏高原及新疆等3个区域. CMIP5的6个模式对东亚区域及其子区域东北亚、青藏高原、新疆1850-2004年降雪量年际变化特征的模拟差异较大. 多模式集合预报的结果表现为, 在过去155 a(1850-2004年)东亚区域降雪量呈现明显减小趋势, 东北亚和青藏高原降雪量为波动略有减小趋势, 新疆降雪量为明显增加趋势.     

Mountain range specific analog weather forecast model for northwest Himalaya in India

Mountain range specific analog weather forecast model is developed utilizing surface weather observations of reference stations in each mountain range in northwest Himalaya (NW-Himalaya). The model searches past similar cases from historical dataset of reference observatory in each mountain range based on current situation. The searched past similar cases of each mountain range are used to draw weather forecast for that mountain range in operational weather forecasting mode, three days in advance. The developed analog weather forecast model is tested with the independent dataset of more than 717 days (542 days for Pir Panjal range in HP) of the past 4 winters (2003–2004 to 2006–2007). Independent test results are reasonably good and suggest that there is some possibility of forecasting weather in operational weather forecasting mode employing analog method over different mountain ranges in NW-Himalaya. Significant difference in overall accuracy of the model is found for prediction of snow day and no-snow day over different mountain ranges, when weather is predicted under snow day and no-snow day weather forecast categories respectively. In the same mountain range, significant difference is also found in overall accuracy of the model for prediction of snow day and no-snow day for different areas. This can be attributed to their geographical position and topographical differences. The analog weather forecast model performs better than persistence and climatological forecast for day-1 predictions for all the mountain ranges except Karakoram range in NW-Himalaya. The developed analog weather forecast model may help as a guidance tool for forecasting weather in operational weather forecasting mode in different mountain ranges in NW-Himalaya.     

Operational forecasting of daily summer maximum and minimum temperatures in the Valencia Region

Extreme-temperature events have a great impact on human society. Thus, knowledge of summer temperatures can be very useful both for the general public and for organizations whose workers operate in the open. An accurate forecasting of summer maximum and minimum temperatures could help to predict heatwave conditions and permit the implementation of strategies aimed at minimizing the negative effects that high temperatures have on human health. The objective of this work is to evaluate the skill of the regional atmospheric and modelling system (RAMS) model in determining daily summer maximum and minimum temperatures in the Valencia Region. For this, we have used the real-time configuration of this model currently running at the Centro de Estudios Ambientales de Mediterráneo Foundation. This operational system is run twice a day, and both runs have a 3-day forecast range. To carry out the verification of the model in this work, the information generated by the system has been broken into individual simulation days for a specific daily run of the model. Moreover, we have analysed the summer forecast period from 1 June to 31 August for 2007, 2008, 2009 and 2010. The results indicate good agreement between observed and simulated maximum temperatures, with RMSE in general near 2 °C both for coastal and inland stations. For this parameter, the model shows a negative bias around ?1.5 °C in the coast, while the opposite trend is observed inland. In addition, RAMS also shows good results in forecasting minimum temperatures for coastal locations, with bias lower than 1 °C and RMSE below 2 °C. However, the model presents some difficulties for this parameter inland, where bias higher than 3 °C and RMSE of about 4 °C have been found. Besides, there is little difference in both temperatures forecasted within the two daily RAMS cycles and that RAMS is very stable in maintaining the forecast performance at least for three forecast days.     

Use of objective analysis to estimate winter temperature and precipitation at different stations over western Himalaya

Temperature and fresh snow are essential inputs in an avalanche forecasting model. Without these parameters, prediction of avalanche occurrence for a region would be very difficult. In the complex terrain of Himalaya, nonavailability of snow and meteorological data of the remote locations during snow storms in the winter is a common occurrence. In view of this persistent problem present study estimates maximum temperature, minimum temperature, ambient temperature and precipitation intensity on different regions of Indian western Himalaya by using similar parameters of the neighbouring regions. The location at which parameters are required and its neighbouring locations should all fall in the same snow climatic zone. Initial step to estimate the parameters at a location, is to shift the parameters of neighbouring regions at a reference height corresponding to the altitude of the location at which parameters are to be estimated. The parameters at this reference height are then spatially interpolated by using Barnes objective analysis. The parameters estimated on different locations are compared with the observed one and the Root Mean Square Errors (RMSE) of the observed and estimated values of the parameters are discussed for the winters of 2007–2008.     

Prediction and error growth in the daily forecast of precipitation from the NCEP CFSv2 over the subdivisions of Indian subcontinent

This study investigates the forecast skill and predictability of various indices of south Asian monsoon as well as the subdivisions of the Indian subcontinent during JJAS season for the time domain of 2001–2013 using NCEP CFSv2 output. It has been observed that the daily mean climatology of precipitation over the land points of India is underestimated in the model forecast as compared to observation. The monthly model bias of precipitation shows the dry bias over the land points of India and also over the Bay of Bengal, whereas the Himalayan and Arabian Sea regions show the wet bias. We have divided the Indian landmass into five subdivisions namely central India, southern India, Western Ghat, northeast and southern Bay of Bengal regions based on the spatial variation of observed mean precipitation in JJAS season. The underestimation over the land points of India during mature phase was originated from the central India, southern Bay of Bengal, southern India and Western Ghat regions. The error growth in June forecast is slower as compared to July forecast in all the regions. The predictability error also grows slowly in June forecast as compared to July forecast in most of the regions. The doubling time of predictability error was estimated to be in the range of 3–5 days for all the regions. Southern India and Western Ghats are more predictable in the July forecast as compared to June forecast, whereas IMR, northeast, central India and southern Bay of Bengal regions have the opposite nature.     

辽宁雪灾区划及降雪影响预评估

根据1951-2014年辽宁省各市、县降雪灾害造成的经济损失、雪灾出现的频率,多年平均灾损强度及气象灾变指数,计算各市、县的气象灾损值,并依此值作为雪灾灾度指数来进行辽宁降雪灾害区划.利用1951-2014年辽宁省58个国家级气象站降雪日数(日降雪量≥5mm)资料和辽宁省降雪灾情资料,采用统计分析、百分位数等方法研究确定不同灾害分区下不同降雪阈值与降雪影响预评估的关系.结果表明:(1)灾害重度区和灾害中度区主要位于辽宁中部城市群或经济发达区域,雪灾多年平均损失为280万元以上,损失程度较重;灾害轻度区和灾害微度区主要位于辽宁东部山区及辽宁西北部山区,雪灾多年平均损失为68万元以上,损失程度较轻;雪灾对辽宁影响最大的受灾体是公路,其次为设施农业.(2)在业务使用中,应用不同灾害分区降雪致灾阈值与受灾体损毁等级的预评估关系,采用定量降水预报自动判断生成降雪灾害预评估结果,能够客观的实现降雪落区不同、服务重点不同,对提高决策气象服务的针对性具有参考作用.     

中国西北干旱区降雪和极端降雪变化特征及未来趋势

降雪是中国西北干旱区水文系统中关键的组成要素, 同时也是对气候变化极为敏感的因子。利用中国西北干旱区的89个气象站点逐日气象资料结合IPCC-CMIP5气候情景数据, 研究了该区域降雪和极端降雪的时空变化特征, 并分析了其对气候变化的响应机理及未来变化趋势。结果表明： 1971—2010年, 我国西北干旱区年降雪量显著增加, 但降雪次数却明显减少; 年极端降雪发生次数占总降雪次数的比例不足3%, 但其对年降雪量的平均贡献可达1/4, 且极端降雪量和发生次数的增加是近40年西北干旱区降雪总量增加的主要原因。极端降雪发生时的气温要比非极端降雪发生时的气温平均高3.3 ℃; 当气温在1 ℃以下, 降雪强度随气温升高而增大, 该变化特征基本符合克劳修斯-克拉伯龙方程理论, 气候变暖是导致极端降雪显著增加的主要原因。在RCP4.5气候情景下, 我国西北干旱区未来年降雪次数将大幅减少, 年降雪量将在（2040±5）年前后达到峰值随后下降, 年极端降雪量和发生次数预计（2060±5）年左右达到峰值; 相比基准期, 2050s西北干旱区所有站点的年降雪发生次数都将明显减少, 区域平均年降雪量将减少5%, 而年极端降雪量和发生次数有微弱的增加, 分别增加约2%和4%。     

1961-2016年中国天山不同级别降雪事件变化特征分析

为了更好地理解降雪对气候变化的响应及机理,利用天山山区及周边49个站点日气象资料,采用参数化降雪判识方案提取降雪序列,以百分位阈值法分级别分析天山山区1961-2016年降雪事件变化特征。结果表明:①天山山区降雪量和降雪频次呈山区大于盆地,北坡大于南坡,自西北向东南递减的分布特征。②过去56年来,天山山区降雪量显著增加,降雪频次微弱增加;各级别降雪量和降雪频次变化趋势表现为:小雪显著减少,中雪变化平稳,大雪和极端降雪显著增加;降雪显著增加区域集中分布于天山北坡中部和伊犁河谷地区,降雪量的增加主要由极端降雪量和频次的增加所致。③年降雪量、大雪降雪量和频次、极端降雪量和频次在20世纪80年代中期发生突变增加,其他级别降雪量和频次无明显突变。④天山山区降雪量和极端降雪量的增加与气温变暖有关。     

Evaluation of the weather research and forecasting (WRF) model over Guyana

In the present study, diagnostic studies were undertaken using station-based rainfall data sets of selected stations of Guyana to understand the variability of rainfall. The multidecadal variation in rainfall of coastal station Georgetown and inland station Timehri has shown that the rainfall variability was less during the May–July (20–30%) of primary wet season compared to the December--January (60–70%) of second wet season. The rainfall analysis of Georgetown based on data series from 1916 to 2007 shows that El Niño/La Niña has direct relation with monthly mean rainfall of Guyana. The impact is more predominant during the second wet season December--January. A high-resolution Weather Research and Forecasting model was made operational to generate real-time forecasts up to 84 h based on 00 UTC global forecast system (GFS), NCEP initial condition. The model real-time rainfall forecast during July 2010 evaluation has shown a reasonable skill of the forecast model in predicting the heavy rainfall events and major circulation features for day-to-day operational forecast guidance. In addition to the operational experimental forecast, as part of model validation, a few sensitivity experiments are also conducted with the combination of two cloud cumulus (Kain--Fritsch (KF) and Betts–Miller–Janjic (BMJ)) and three microphysical schemes (Ferrier et al. WSM-3 simple ice scheme and Lin et al.) for heavy rainfall event occurred during 28–30 May 2010 over coastal Guyana and tropical Hurricane ‘EARL’ formed during 25 August–04 September 2010 over east Caribbean Sea. It was observed that there are major differences in the simulations of heavy rainfall event among the cumulus schemes, in spite of using the same initial and boundary conditions and model configuration. Overall, it was observed that the combination of BMJ and WSM-3 has shown qualitatively close to the observed heavy rainfall event even though the predicted amounts are less. In the case of tropical Hurricane ‘EARL’, the forecast track in all the six experiments based on 00 UTC of 28 August 2010 initial conditions for the forecast up to 84 h has shown that the combination of KF cumulus and Ferrier microphysics scheme has shown less track errors compared to other combinations. The overall average position errors for all the six experiments taken together work out to 103 km in 24, 199 km in 48, 197 km in 72 and 174 km in 84 h.     

辽宁省不同等级降雪变化特征

利用辽宁省52个站逐日降水量及降雪天气现象资料提取出逐日降雪数据,采用多种统计方法分析了近53 a(1961-2013年)不同等级降雪的时空变化特征,研究表明:降雪量和降雪日数空间分布上山地要大于平原地区,由东部山区向沿海地区减少;降雪强度中心位于辽宁中部城市群所在的平原地区。降雪量、降雪日数年内分配分别呈双峰型和单峰型分布,中雪等级以上的降雪多发生在冬末春初。年降雪量增加,年降雪日数(降雪强度)显著减少(减小);降雪日数的显著减少主要表现为微量降雪日数和小雪日数的减少,尤其是微量降雪日数,降雪强度的显著增大主要是暴雪强度的增大。1960s和1970s为降雪偏多时段,1990s以来降雪量增加,降雪日数减少。不同区域各级降雪占总降雪的比例,辽东地区以微量降雪日数最大,其他区域均以小雪日数和暴雪降雪量最大。全省降雪量有65.4%站点呈增加趋势,降雪日数96.2%的站点呈减少趋势,降雪强度90.4%站点呈增大趋势,辽西地区降雪变率要大于辽东山区。小雪降雪量和微量降雪日数贡献率均呈下降趋势,其他不同等级降雪贡献率均呈上升趋势。随着纬度升高(海拔增高),总降雪量(降雪日数)和各等级降雪量(降雪日数)均增加,总降雪强度和小雪强度减小。     

1971-2010年青藏高原冬季降雪气候变化及空间分布

利用青藏高原55个气象站1971-2011年冬季（12月-翌年2月）逐月降雪量资料分析了冬季降雪的气候特征,得到高原冬季降雪总体上呈现东部和南部多、西北部和雅鲁藏布江中段少雪的分布特征,相对变率分布与降雪的分布几乎相反且变率大,以30°N为界高原降雪存在南北反相的变化趋势即北部降雪有所增加而南部减少.用旋转经验正交函数REOF结合相关分析进行降雪分区的基础上,重点分析了近40 a来高原降雪的演变特征和长期气候趋势.结果表明：降雪分布清楚地反映了高原的地理特征和气候特点,即高原南部迎风坡、冷暖气流交汇处降雪多,而背风坡、北部降雪少;近40 a降雪呈现“少-多-少”趋势,1980-1990年代期间降雪明显偏多,大约1970年代中期发生了由少雪到多雪的突变现象,其中南部2个区分别在2007年和1988年出现了降雪减少的突变现象;降雪具有显著的准14 a年代际变化和准8 a周期变化,且存在年代际特征.     

The sensitivity to the microphysical schemes on the skill of forecasting the track and intensity of tropical cyclones using WRF-ARW model

The Advanced Research WRF (ARW) model is used to simulate Very Severe Cyclonic Storms (VSCS) Hudhud (7–13 October, 2014), Phailin (8–14 October, 2013) and Lehar (24–29 November, 2013) to investigate the sensitivity to microphysical schemes on the skill of forecasting track and intensity of the tropical cyclones for high-resolution (9 and 3 km) 120-hr model integration. For cloud resolving grid scale (<5 km) cloud microphysics plays an important role. The performance of the Goddard, Thompson, LIN and NSSL schemes are evaluated and compared with observations and a CONTROL forecast. This study is aimed to investigate the sensitivity to microphysics on the track and intensity with explicitly resolved convection scheme. It shows that the Goddard one-moment bulk liquid-ice microphysical scheme provided the highest skill on the track whereas for intensity both Thompson and Goddard microphysical schemes perform better. The Thompson scheme indicates the highest skill in intensity at 48, 96 and 120 hr, whereas at 24 and 72 hr, the Goddard scheme provides the highest skill in intensity. It is known that higher resolution domain produces better intensity and structure of the cyclones and it is desirable to resolve the convection with sufficiently high resolution and with the use of explicit cloud physics. This study suggests that the Goddard cumulus ensemble microphysical scheme is suitable for high resolution ARW simulation for TC’s track and intensity over the BoB. Although the present study is based on only three cyclones, it could be useful for planning real-time predictions using ARW modelling system.     

Evaluation of statistical bias correction methods for numerical weather prediction model forecasts of maximum and minimum temperatures

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.