Impact of ERS-2 Scatterometer wind data on Global Analysis-Forecast system

The near surface scatterometer wind data from the European remote sensing satellite ERS-2 of European space agency(ESA) became available at NCMRWF on real time basis since February 1997. An attempt has been made to assimilate this data in the global data assimilation system(GDAS) operational at NCMRWF after proper quality control to study its impact on the analysis as well as on medium range weather forecast over the tropics. For this purpose the GDAS was run for 15 days (27 May to 10 June 1998). The impact has been examined through circulation characteristics and various objective scores. The study revealed that with proper quality control the scatterometer wind data can be assimilated in real time basis, resulting in an overall improvement in performance of the analysis-forecast system.

Evaluation of Forecast Performance for Four Meteorological Models in Summer Over Northwestern China

At present, numerical models, which have been used for forecasting services in northwestern China, have not been extensively evaluated. We used national automatic ground station data from summer 2016 to test and assess the forecast performance of the high-resolution global European Centre for Medium-Range Weather Forecast (ECMWF) model, the mesoscale Northwestern Mesoscale Numerical Prediction System (NW-MNPS), the global China Meteorological Administration T639 model, and the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model over northwestern China. The root mean square error (RMSE) of the 2-m temperature forecast by ECMWF was the lowest, while that by T639 was the highest. The distribution of RMSE for each model forecast was similar to that of the difference between the modeled and observed terrain. The RMSE of the 10-m wind speed forecast was lower for the global ECMWF and T639 models and higher for the regional NW-MNPS and GRAPES models. The 24-h precipitation forecast was generally higher than observed for each model, with NW-MNPS having the highest score for light rain and heavy storm rain, ECMWF for medium and heavy rain, and T639 for storm rain. None of the models could forecast small-scale and high-intensity precipitation, but they could forecast large-scale precipitation. Overall, ECMWF had the best stability and smallest prediction errors, followed by NW-MNPS, T639, and GRAPES.

Verification and usability of location specific medium range weather forecast for Kullu valley

Weather plays a crucial role in agriculture. Precipitation, temperature, humidity, wind speed and direction, drying conditions, dry and wet spells are the most important weather elements information about whom could play a significant role in farm planning and operations. Inclement weather events like drought and floods, cold and heat waves, hails, squalls, tropical storms severely affect the production. Occurrences of erratic weather are beyond human control. It is possible to adapt or mitigate their malevolent effect to some extend if the occurrence of the events is predicted well in advance and farmers are suitably advised to take ameliorative measures. Attempts were made to verify the weather forecasts received on every Tuesday and Friday from NCMRWF/IMD. The verification analysis was carried out on weekly, seasonal and annual basis using various verification techniques, viz., Ratio Score (RS), Critical Success Index (CSI), Heidke Skill Score (HSS), Hanssen and Kuipers Score (HK), Root Mean Square Error (RMSE), usability analysis and correlation approach during 2000-01 to 2009-10. The analysis depicted that ratio score on yearly basis was highest (74.6) during 2005-06 followed by 2004-05 (72.9) and 2003-04 (72.7). The value of H.K. score ranged between 24 and 42. The forecast found within quite usability range for most of the parameters but improvements are still possible. The correlation analysis showed that there was high correlation between observed and predicted values over the years. Hence, the forecast was found widely applicable among different user groups.

Statistical interpretation of general circulation model: A prospect for automation of medium range local weather forecast in India

The General Circulation Models (GCM), though able to provide reasonably good medium range weather forecast. have comparatively less skill in forecasting location-specific weather. This is mainly due to the poor representation of 16cal topography and other features in these models. Statistical interpretation (SI) of GCM is very essential in order to improve the location-specific medium range local weather forecast. An attempt has been made at the National Centre for Medium Range Weather Forecasting (NCMRWF), New Delhi to do this type of objective forecasting. Hence location-specific SI models are developed and a bias free forecast is obtained. One of the techniques for accomplishing this, is the Perfect Prog. Method (PPM). PPM models for precipitation (quantitative, probability, yes/no) and maximum minimum temperature are developed for monsoon season (June to August) for 10 stations in lndia. These PPM models and the output from the GCM (R-40) operational at NCMRWF, are then used to obtain the SI forecast. An indirect method based upon SI forecast and observed values of previous one or two seasons, for getting bias free forecast is explained. A comparative study of skill of bias free SI and final forecast, with the observed, issued from NCMRWF to 10 Agromet Field Units (AMFU) during monsoon season 1993, has indicated that automation of medium range local weather forecast can be achieved with the help of SI forecast.

Quantifying Temperature and Precipitation Change Caused by Land Cover Change: A Case Study of India Using the WRF Model

The large-scale Land-Uses and Land-Cover Changes (LULCC) in India in the past several decades is primarily driven by anthropogenic factors that influence the climate from regional to global scales. Therefore, to understand the LULCC over the Indian region from 2002 to 2015 and its implications on temperature and precipitation, we performed Weather Research Forecast (WRF) model simulation using the European Centre for Medium-Range Weather Forecast (ECMWF) reanalysis data for the period 2009 to 2015 as a boundary condition with 2009 as spin-up time. The results showed moderate forest cover loss in major parts of northeast India, and the Himalayan region during 2002–2015. Such large LULC changes, primarily significant alteration of grassland and agriculture from the forest, led to increased precipitation due to increasing evapotranspiration (ET) similar to the forest-dominated regions. An increase in the precipitation patterns (>300 mm) was observed in the parts of eastern and western Himalayas, western Ghats, and the northwestern part of central India, while most parts of northeast Himalayas have an exceptional increase in precipitation (∼100–150 mm), which shows similar agreement with an increase of leaf area index (LAI) by ∼15%. The overall phenomenon leads to a greening-induced ET enhancement that increases atmospheric water vapor content and promotes downwind precipitation. In the case of temperature, warming was observed in the central to eastern parts of India, while cooling was observed in the central and western parts. The increase in vegetated areas over northwest India led to an increase in ET, which ultimately resulted in decreased temperature and increased precipitation. The study highlights the changes in temperature and precipitation in recent decades because of large LULCC and necessitates the formulation of sustainable land use-based strategies to control meteorological variability and augment ecological sustainability.

Modeling the effect of planting date on Maize (Zea mays L.) crop in Sabour region of Bihar using DSSATv3.5 crop simulation model

CERES-Maize model calibrated for local conditions of Sabour has been used to evaluate the relevance medium range weather forecast relative to the maize crop growth period. The procedure is to place the reference year's daily weather into the model up to the time the yield prediction is to be made and sequences of historical data (one sequence per year) after that time until the end of growing season to give yield estimates. A procedure that makes use of historical weather data, medium range weather forecast (mrwf) and current weather data in conjunction with the CERES-Maize model was developed to arrive at a probable distribution of predicted yields. The lower temperature and more solar radiation in tassel emergence to dough stage silk emergence to physiological maturity phase and lower maximum temperature are found favorable to contribute more in increasing the grain yields. The CERES- Maize model correlated for the genetic coefficient predicts the silking dates and physiological maturity very well. Kharif maize gave the highest grain yield of 3490 kg/ha in 1999 and the lowest of 2474 kg/ha in 1979. Among eight different sowing dates the lowest average grain yield was 3190 kg/ha for the last sowing date and the highest average grain yield was 3313 kg/ha in 2nd sowing date. The 25 percentiles were less than the mean grain yields and also 75 percentiles.

Analysis of Vortex Development over Eastern Indian Ocean using Potential Vorticity

A vortex phenomenon may have a significant influence, especially on wind circulation patterns and extreme weather in Indonesia. The formation of the vortex, initially located over the eastern part of the Indian Ocean has drawn attention due to the highest frequency of its occurrence and as the source of the vortex over the Indonesian region. Vortices generated in this region is also suspected as one of contributing factor for flooding events at Jakarta in 2002 and 2007, studying both formation and development mechanism of these vortices is essential. The evolution of vortex development is investigated to characterize the vortex motion and development pattern in the Eastern Indian Ocean region. The study was conducted for 17 years starting from 1998 to 2016 on every December-January-February (DJF) period using ECMWF (European Center for Medium-Range Weather Forecast) ERA-Interim Reanalysis data. The analysis of vortex evolution was conducted for each event using a composite evolution of potential vorticity anomalies in the isentropic layer. The result shows 84 vortex systems identified with three characteristic patterns of vortex movement that occurred during 295 days of the observation period. Composite analysis of potential vorticity anomalies shows that the initial formation of vortices in the Eastern Indian Ocean is related to the emergence of negative potential vorticity anomalies from the west, which subsequently forming the vortices.

Influence of Swell Wave on Wind Speed Retrieval Using ENVISAT ASAR Wave Mode Imagery

The main work of this paper is to explore the influence of swell wave on retrieval of wind speed using ENVISAT ASAR wave mode imagery. The normalized radar cross section (NRCS) scene under different sea states is simulated to investigate the relationship between NRCS variation with swell height, together with swell direction. Moreover, the key parameter of imagery variance (Cvar) is selected to describe the swell wave on SAR imagery. In addition, the imagery parameters of skewness and kurtosis are together analyzed as a function of collocated significant swell wave height and wind speed. Based on the analyzed results, a new method for wind speed retrieval is proposed using ENVISAT ASAR, namely, F(n). Besides the CMOD parameters of NRCS, incidence angle, and relative wind direction, the imagery parameters of Cvar, skewness, and kurtosis are used to compensate for the influence of swell wave on wind speed retrieval in F(n). Finally, the collocated European Centre for Medium-Range Weather Forecast (ECMWF) wind speed dataset and ENVISAT ASAR wave mode imagery are used to verify the retrieval precision and compare with CMOD functions. It is concluded that the F(n) model performs much better than other CMOD functions, with a correlation of 0.89, a bias of 0.08, a RMSE of 1.2 m/s, and an SI of 0.1.