India Meteorological Department Data Research Articles

Characteristics of extreme rainfall in different gridded datasets over India during 1983–2015

Understanding the spatial and temporal occurrences of extreme rainfall events and their changes is crucial for reducing the risk associated with extreme events, especially under anthropogenic warming. This study presents a comparative analysis of twelve gridded rainfall datasets in representing the spatial and temporal variation of extreme rainfall events and trends in the recent past (1983–2015) in India. The selected datasets fall into four categories: gauge-based (APHRODITE, CPC, GPCC, REGEN), satellite-derived (CHIRPS, PERSIANN-CDR), reanalysis (ERA5, MERRA-2, PGF, and JRA-55), and product merged from these three types (WFDEI and MSWEP). For comparison, a gauge-based, high resolution (0.25° × 0.25°) daily gridded rainfall dataset, prepared by the India Meteorological Department (IMD) is used as a reference dataset. Eleven extreme climate indices, defined by the World Meteorological Organization's Expert Team on Climate Change Detection and Indices (ETCCDI) and the IMD are used to represent the magnitude, frequency, and duration of extreme rainfall events in India. The relative performance of these datasets in capturing extreme rainfall events is evaluated by computing model evaluation matrices i.e. correlation coefficient (CC) and Percentage Bias (PBIAS). Trends in extreme rainfall indices and their magnitudes are calculated using Mann-Kendell and Sen's slope methods respectively, to compare with the IMD dataset. Our study finds large uncertainties in representing extreme rainfall events where in most cases the datasets underestimate higher extreme events compared to the IMD data. GPCC and MSWEP better capture the magnitude, duration, and frequency of extreme rainfall events in India and are comparable to the pattern of the IMD dataset. CHIRPS and ERA5 perform better than other satellite and reanalysis rainfall datasets. All these selected datasets consistently underestimate extreme rainfall events over the northern Himalayan region. The gridded datasets are unable to capture the spatial pattern of observed trends in extreme rainfall indices in India when compared to the IMD dataset.

Dynamical and thermodynamical aspects of precipitation events over India

AbstractThis study examines different precipitation events (PEs) for 1951–2015 using 0.25°×0.25° India Meteorological Department data and diagnoses their associated dynamical and thermodynamical processes during the summer monsoon season over India using ERA5 reanalysis. Our results reveal a significant decreasing trend of mean precipitation, rain event days, light rain events and moderate rain events (MREs) for the overall period. However, the frequency of heavy rain events (HREs) and very heavy rain events (VhREs) has increased. Pettit's test for all PEs and mean precipitation indicates the changes nearly in the 1970s. In spite of this, the rate of change of LREs, MREs and HREs was increased by 17, 91 and 114% after the observed change point with respect to before change point, while for VhREs, this was reduced by 50%. These decreasing (increasing) trends of LREs (VhREs) are attributed to both thermodynamical and dynamical conditions. LREs are driven mainly by convective precipitation having a contribution of ~79%, whereas large‐scale precipitation contributes ~56% to the VhREs. It is observed that the dynamical (thermodynamical) effect largely drives VhREs (LREs) PEs. During VhREs, stronger moisture‐laden low‐level jet (LLJ) from the surrounding ocean converges over the Indian land mass, facilitating favourable conditions for enhanced precipitation. Simultaneously, weaker convective available potential energy (CAPE) during the VhREs underlines inconstancy in thermodynamics. Stronger LLJ and weaker CAPE during the extremes and vice versa for LREs highlight the dominance of the dynamical (thermodynamical) effect during the VhREs (LREs).

Simulation of Indian Summer Monsoon Rainfall (ISMR) with fully coupled regional chemistry transport model: A case study for 2017

The Indian subcontinent is a major hotspot of enhanced aerosol loading and emission of various chemical compounds due to extensive anthropogenic activities that profoundly impact the hydrological cycle of this region. Using coupled regional atmospheric chemistry transport model, WRF-Chem (Weather Research and Forecasting model coupled with chemistry) with fully interactive chemistry and dynamics, we carried out two sets of 122-days simulation experiments: (1) a control experiment without embedding chemistry and (2) a sensitivity experiment with embedding fully coupled MOZART-MOSAIC chemistry, to examine the impact of chemistry on the Indian Summer Monsoon Rainfall (ISMR) of 2017. The results show that inclusion of chemistry in the model plays a role in altering the spatial distribution of ISMR, reflected by a reduced mean rainfall by about 3 mm/day over the Indian subcontinent. The mean bias of model simulated rainfall reduces from 2.73 mm/day (excluding chemistry) to 1.6 mm/day (including chemistry), and from 1.2 mm/day (excluding chemistry) to 0.81 mm/day (including chemistry) when compared with IMD (India Meteorological Department) and TRMM (Tropical Rainfall Measuring Mission) rainfall data, respectively. Additionally, we found that inclusion of interactive chemistry causes a decrease in the daily convective rainfall by about 3 mm that is contributing largely to the mean rainfall differences. Further, analysis of hydrometeors indicates that the chemistry interacts with the cloud microphysics by modulating the hydrometeor paths of different cloud hydrometeors that consequently impacts the simulation of ISMR over this region. Results indicate that the virtue of detailed and more realistic representation of chemistry in the model has led to more realistic simulation and cannot be ignored in numerical forecasting of ISMR.

Spatio‐temporal variability of summer monsoon surface air temperature over India and its regions using Regional Climate Model

AbstractIn this study, a dynamically downscaled regional climate model (RegCM4.3) is used to study the Indian summer monsoon (ISM) surface air temperature over the South‐Asia CORDEX domain using six convection schemes during 1986–2010. The spatial and temporal variability of mean surface air temperature has been analysed with reference to the India Meteorological Department (IMD) analysis data using various statistical scores. The sensitivity experiments in selecting the best convective parameterized schemes have been performed in simulating the surface air temperature during the summer monsoon season (June–September) over India and its five sub‐regions such as Northwest India, Northcentral India, West Peninsular India, Eastern Peninsular India, and Southern Peninsular India. The model results show the tendency of overestimation of surface air temperature mainly in four cumulus parameterization schemes (CPSs) that is, Tiedtke, Emanuel, Mix98, and Mix99 of RegCM4.3 during the JJAS, where Grell and Kuo CPSs show better agreement with the IMD data. Overall, Grell CPS has a close resemblance to the observation data with a minimum root mean square error, mean absolute error, lowest mean absolute percentage error (MAPE), and higher correlation coefficient. The model simulated results have also been investigated further using modified Nash Sutcliffe efficiency and modified Willmott's degree of index. These analyses confirm the potentiality of the Grell CPS followed by the Kuo CPS in simulating interannual variability of the surface air temperature over Indian and its five sub‐regions. The MAPE in Grell and Kuo CPSs are 0.004 and 0.013°C during monsoon season over India, respectively. The inter‐scheme difference in simulating surface air temperature is linked with the generation of low cloud convection and warming‐induced atmospheric moisture advection in the schemes. Therefore, Emanuel, Tiedtke, and Mix98 CPSs have shown a persistent nature of overestimation in surface air temperature variability during JJAS. It is also inferred that after removing the systematic mean bias from the RegCM4.3 model simulated outputs; the skill of Emanuel, Mix98, and Mix99 could be useful over the Indian subcontinent except for the southern peninsular region.

Spatio–temporal rainfall trend anomalies in Vidarbha region using historic and predicted data: a case study

The present study aims to determine the temporal variations of rainfall trend in the Vidarbha region, India as a prerequisite for the flood–drought optimization of the area. Historic rainfall data from India Meteorological Department (IMD) and Indian Institute of Tropical Meteorology (IITM), Pune, were taken for a time period of 1951–2005 and is correlated. Data from IITM is a variable resolution Global Model, based on the Laboratorie Dynamique Meteorologie, France (LMDz). The correlation of LMDz model data with the IMD data was calculated using four statistical variables: Correlation Coefficient (CC), Normalized Root Mean Square Deviation (NRMSD), Nash–Sutcliffe Efficiency (NSE), and Skill Score (SS). Both data were further used for calculating the rainfall trend using non-parametric Mann–Kendall test and Sen’s Slope estimator and results were compared. There observed a good correlation between the data series from IMD and IITM. Hence projected rainfall data from IITM, for a period of 2020–2095 has been used to get a line about the future rainfall pattern of the area. The future rainfall trend scenario of Vidarbha is not favorable; hence proper water resources strategy should be defined to avoid desertification of the area.

Assessing Historical Variability of South Asian Monsoon Lows and Depressions With an Optimized Tracking Algorithm

AbstractCyclonic low‐pressure systems (LPS) produce abundant rainfall in South Asia, where they are traditionally categorized as monsoon lows, monsoon depressions, and more intense cyclonic storms. The India Meteorological Department (IMD) has tracked monsoon depressions for over a century, finding a large decline in their number in recent decades, but their methods have changed over time and do not include monsoon lows. This study presents a fast, objective algorithm for identifying monsoon LPS and uses it to assess interannual variability and trends in reanalyses. Variables and thresholds used in the algorithm are selected to best match a subjectively analyzed LPS data set while minimizing disagreement between four reanalyses in a training period. The stream function of 850 hPa horizontal wind is found to be optimal in this sense; it is less noisy than vorticity and represents the complete nondivergent wind, even when flow is not geostrophic. Using this algorithm, LPS statistics are computed for five reanalyses, and none show a detectable trend in monsoon depression counts since 1979. Both the Japanese 55‐year Reanalysis (JRA‐55) and the IMD data set show a step‐like reduction in depression counts when they began using geostationary satellite data, in 1979 and 1982, respectively; the 1958–2018 linear trend in JRA‐55, however, is smaller than in the IMD data set, and its error bar includes 0. There are more LPS in seasons with above‐average monsoon rainfall and in La Niña years, but few other large‐scale modes of interannual variability are found to modulate LPS counts, lifetimes, or track length consistently across reanalyses.

Spatiotemporal analysis of extreme indices derived from daily precipitation and temperature for climate change detection over India

The study analysed the spatiotemporal variation in extreme precipitation and temperature at the daily scale across India using eight indices of climate change suggested by the Expert Team on Climate Change Detection and Indices (ETCCDI). For this analysis, latest high-resolution India Meteorological Department (IMD) data for the period 1971–2017 (precipitation) and 1971–2013 (temperature) are used along with global gridded reanalysis products. The trends are evaluated using non-parametric Mann-Kendall (MK) test and regression analysis. At the annual scale, about 13% of the locations indicated significant trend (either increasing or decreasing at 5% significance level) in the index R95p (rainfall contribution from extreme ‘wet days’) while 20% of the locations indicated significant trend in R5p (rainfall contribution from extreme ‘dry days’). For the seasonal analysis (June to September), the corresponding figures are nil and 21% respectively. The number of ‘warm days’ per year increased significantly at 14% of the locations, while the number of ‘cold days’, ‘warm nights’ and ‘cold nights’ per year decreased significantly at several (42%, 34% and 39%) of the locations. The extreme temperature indices for the future (using CanESM2 projected data for RCP8.5 after suitable bias correction) show significant increasing (decreasing) trend in warm days (cold days) in most (49% to 84%) of the locations. Further, most locations (varying from 60 to 81%) show an increasing trend in warm nights and a decreasing trend in cold nights. Similar analysis for the historical and future period are also performed using Climate Prediction Centre (CPC) reanalysis data as the reference and the trends, on comparison with IMD data, seem to be in agreement for temperature extremes but spatially more extensive in case of CPC precipitation extremes.

Evaluation of season long rainfall simulated by WRF over the NWH region: KF vs. MSKF

Abstract This study is aimed to assess the performance of Numerical Weather Predication (NWP) model Weather Research and Forecasting (WRF) in simulating the season long rainfall pattern over the North-West Himalayan (NWH) region. For this purpose, two experiments are designed with identical initial conditions but varying cumulus physics options viz.; Kain-Fritsch (KF) and Multi-scale Kain-Fritsch (MSKF). WRF simulations have been carried out for multiple nested domains (45, 15 and 5 km) configuration for monsoon season of 2017. Model derived rainfall corresponding to two experiments has been evaluated against the gridded rainfall data prepared by India Meteorological Department (IMD) and satellite based GPM-IMERGE and INSAT-3D HE-rain. Present results unveiled that the KF scheme overestimates the amount of rainfall over the entire study domain; whereas, MSKF scheme is capable to simulate the magnitude of rainfall comparable to the observations. Model simulated rainfall corresponding to both the experiments yields stronger correlation with satellite based GPM-IMERG as compared to the IMD data. It is noted based on statistically robust Chow test that the WRF simulation with KF scheme estimates break point in the rainfall pattern at 1200 m whereas WRF simulation with MSKF scheme resolves break point at 2000 m which is in close agreement with the previous studies. Categorical statistics metrics and qualitative analysis of WRF simulations suggest that the scale aware cumulus scheme MSKF performs better in resolving monsoon season rainfall pattern over the rugged topography of the NWH region. Present study has implications for improved understanding of spatial pattern of monsoon rainfall over the NWH region.

Drought characterization over India under projected climate scenario

The study evaluates the drought characteristics in India over projected climatic scenarios in different time frames, that is, near‐future (2010–2039), mid‐future (2040–2069), and far‐future (2070–2099) in comparison with reference period (1976–2005). Standardized Precipitation Evapotranspiration Index (SPEI), a multi‐scalar drought index was used owing to its robustness in capturing drought conditions while accounting the temperature. Gridded rainfall and temperature data provided by India Meteorological Department (IMD) was used to perform bias correction of nine Global Climate Models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5) project. Quantile mapping was used to correct the daily rainfall data at seasonal scale whereas daily temperature data was corrected at monthly scale. Multi‐Model Ensemble (MME) was prepared for different homogeneous monsoon regions of India, namely Hilly Regions (HR), Central Northeast (CNE), Northeast (NE), Northwest (NW), West Central (WC), and Peninsula (PS). Taylor diagram statistics were used for the preparation of MME. The regional climate cycle obtained from MME was found to be in good agreement with observed cycle derived from IMD data. The Mann–Kendal trend test was employed to detect the trend in drought severity and magnitude whereas L‐moments based frequency analysis was used to assess the magnitude of extreme drought severity under different time frames. The study reveals an increasing trend in drought severity, duration, occurrences, and the average length of drought under warming climate scenarios. Furthermore, the area under “above moderate drought” (i.e., severe and extreme drought combined) condition was also found to be increasing in projected climate.

Intercomparison of trend analysis of Multisatellite Monthly Precipitation Products and Gauge Measurements for River Basins of India

This study compares the precipitation trend from the gridded rain gauge data collected by India Meteorological Department (IMD) and Multisatellite High Resolution Precipitation Products (HRPPs) including Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) version 7, Climate Prediction Center Morphing (CMORPH) version 1.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1 for the river basins of India. The IMD and HRPPs are of the same spatial resolution (0.25° × 0.25°) and extend from 1998 to 2015. The gridded precipitation datasets are compared for 25 river basins of India. TRMM, CMORPH, PERSIANN, and MSWEP datasets accuracy for the river basins are assessed by comparison with IMD using root mean square error (RMSE) and correlation coefficient (CC) methods. The Mann–Kendall (MK) and modified Mann–Kendall (MMK) tests are applied for analyzing the data trend, and the change is detected by Sen's Slope using all datasets for annual and seasonal time periods. Variation in precipitation is high (>20%) in the northern part of India in all datasets. All these basins located at the elevations above 2000 m. The annual and monsoon trend pattern for TMPA, CMORPH, PERSIANN, and MSWEP matched with IMD data in the north, northwest, and central part in 1–2, 22–25, and parts of 3, 12, and 21 river basins (1998–2015). The calculated results implied that the TMPA precipitation product (in terms of accuracy) and PERSIANN (in terms of annual and monsoon trend) show a better agreement with IMD and they can be used in climate studies and hydrological simulation in locations/river basins where the number of rain gauges is not adequate to quantify the spatial variability of precipitation.

Comparison of regional and seasonal changes and trends in daily surface temperature extremes over India and its subregions

Regional changes in surface meteorological variables are one of the key issues affecting the Indian subcontinent especially in recent decades. These changes impact agriculture, health, water, etc., hence important to assess and investigate these changes. The Indian subcontinent is characterized by heterogeneous temperature regimes at regional and seasonal scales. The India Meteorological Department (IMD) observations are limited to recent decades as far as its spatial distribution is concerned. In particular, over Hilly region, these observations are sporadic. Due to variable topography and heterogeneous land use/land cover, it is complex to substantiate impacts. The European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim (ERA-I) reanalysis not only covers a larger spatial domain but also provides a greater number of inputs than IMD. This study used ERA-I in conjunction with IMD gridded data to provide a comparative assessment of changing temperature patterns over India and its subregions at both regional and seasonal scales. Warming patterns are observed in both ERA-I and IMD data sets. Cold nights decrease during winter; warm days increase and warm spell duration increased during winter could become a cause of concern for society, agriculture, socio-economic reasons, and health. Increasing warm days over the hilly regions may affect the corresponding snow cover and thus river hydrology and glaciological dynamics. Such changes during monsoon are slower, which could be attributed to moisture availability to dampen the temperature changes. On investigation and comparison thereon, the present study provisions usages of ERA-I-based indices for various impact and adaptation studies.

Cloud computing for energy requirement and solar potential assessment

The objective of this research is to derive an approach for the assessment of solar potential using cloud computing for a better energy planning. This approach is used to calculate energy requirement and solar potential having precise prediction probability. The energy requirement has been calculated based on the inputs such as the number of fans, tube lights, and electric pump with their wattage and usage hours. The assessment of the solar potential is based on the input parameters such as Global Horizontal Irradiance (GHI), sunshine hours, India Meteorological Department (IMD) data, cloud cover, tilted irradiance, etc. It is executed by software developed in Eclipse IDE (Integrated Development Environment), an open-source toolkit. Perl script has been used to convert the GHI onto the tilted surface which efficiently quantifies the collected solar irradiance. The IMD data are used for predicting the number of cloudy and rainy days for further estimation of solar potential. Cloud computing is used in uploading of the software module on the cloud. Google App Engine is used to deploy project information on the cloud. It has been found that enough solar potential is available to install Solar Photovoltaic (SPV) modules at Meerpur, India using software tool developed.

Evaluation of Satellite-Derived Rainfall Estimates for an Extreme Rainfall Event over Uttarakhand, Western Himalayas

The extreme rainfall event during June 2013 in the Western Himalayas caused widespread flash floods, which triggered landslides, a lake-outburst, and debris flow. For the hydrological study of such an unexpected extreme event, it is essential to have reliable and accurate rainfall predictions based on satellite observations. The mountainous state of Uttarakhand is covered by complex topography, and this state has few, unevenly distributed, rain gauge networks. This unique study was conducted to evaluate three satellite based rainfall products (i.e., TMPA-3B42, Global Satellite Mapping of Precipitation (GSMaP), and NOAA CPC Morphing Technique (CMORPH)) against the observed rain gauge-based India Meteorological Department (IMD) gridded dataset for this rainfall episode. The results from this comprehensive study confirmed that the magnitude of precipitation and peak rainfall intensity were underestimated in TMPA-3B42 and CMORPH against gauge-based IMD data, while GSMaP showed dual trends with under- and over-predictions. From the results of the statistical approach on the determination of error statistic metrics (MAE (mean absolute error), NRMSE (normalized root mean square error), PBIAS (percent bias), and NSE (Nash-Sutcliffe efficiency)) of respective satellite products, it was revealed that TMPA-3B42 predictions were more relevant and accurate compared to predictions from the other two satellite products for this major event. The TMPA-3B42-based rainfall was negatively biased by 18%. Despite these caveats, this study concludes that TMPA-3B42 rainfall was useful for monitoring extreme rainfall event in the region, where rain-gauges are sparse.

A comparative assessment of temperature data from different sources for Dehradun, Uttarakhand, India

A comparative study of extreme temperature parameters from different sources is carried out by examining standardized anomalies, trends, correlation, and equivalence of datasets. Maximum temperature (Tmax) and minimum temperature (Tmin) for Dehradun, from two different sources such as computed and gridded data from Climatic Research Unit (CRU) and observed data from India Meteorological Department (IMD) are used for 1901–2012. The CRU data are compared initially with IMD, by graphical assessment of standardized anomalies. Subsequently, change points are identified by using Cumulative Sum (CUSUM)-chart technique for trend analysis. The magnitude and significance of trends are determined by applying Sen’s slope test, and on the basis of these, trends are compared. Further, correlation analysis is carried out and datasets are tested for equivalence by using Wilcoxon–Mann–Whitney test. The result shows that annual standardized anomalies of CRU data follow the pattern of annual standardized anomalies of IMD data. The CRU data exhibit similar trends and are well correlated with IMD dataset. Moreover, CRU anomaly data are identical with IMD anomaly data in the recent decades. High resolution gridded CRU data have open access and may be more useful due to its spatio-temporal continuity for land areas of the world.

Monsoon Variability, the 2015 Marathwada Drought and Rainfed Agriculture

The impact of the drought of the summer monsoon of 2015 has been particularly large in the Marathwada region of Maharashtra which is now facing unprecedented water scarcity and more than one thousand farmers have committed suicide. Substantial losses in the production of important crops such as pulses have been reported in Maharashtra. Naturally, the Marathwada drought has been extensively covered in the print and electronic media. The large impact has been attributed to exceptionally large deficit in rainfall by some journalists and politicians, to two successive droughts in 2014 and 2015 by some and some have considered the drought to be a manifestation of climate change. In this article, we present an analysis of the Marathwada monsoon rainfall from 1871 onwards and show that the quantum of deficit rainfall in 2015 as well as the occurrence of two successive droughts is within the observed variability of the Marathwada rainfall and the 2015 monsoon rainfall also cannot be considered as a manifestation of climate change. Thus the large impact of 2015 is a reflection of poor management of water resources and agriculture, despite the long experience of rainfall variability. We show that the prediction by the India Meteorological Department (IMD) of a high chance of below normal rainfall or a drought on the all-India scale and the occurrence of El Nino could have been used to anticipate large deficiency in Marathwada rainfall. We suggest that the problem of lack of progress in the production of rainfed crops such as pulses has to be addressed by using the rich rainfall data sets in the country to generate information which can be used by farmers and agricultural scientists to identify strategies, which are tailored to the entire spectrum of rainfall variability experienced. Towards this end an interactive software `RAINFO' has been developed at the Indian Institute of Tropical Meteorology ((IITM), Pune to provide location-specific information derived from the IMD data, on the desired facets of rainfall variability.

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008–2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5° resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s−1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3–36 % in track, 6–63 % in CSLP, 26–103 % in MSW and 11–223 % in the radius of maximum winds in 24–96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

Precipitation climatology over India: validation with observations and reanalysis datasets and spatial trends

Changing rainfall patterns have significant effect on water resources, agriculture output in many countries, especially the country like India where the economy depends on rain-fed agriculture. Rainfall over India has large spatial as well as temporal variability. To understand the variability in rainfall, spatial–temporal analyses of rainfall have been studied by using 107 (1901–2007) years of daily gridded India Meteorological Department (IMD) rainfall datasets. Further, the validation of IMD precipitation data is carried out with different observational and different reanalysis datasets during the period from 1989 to 2007. The Global Precipitation Climatology Project data shows similar features as that of IMD with high degree of comparison, whereas Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation data show similar features but with large differences, especially over northwest, west coast and western Himalayas. Spatially, large deviation is observed in the interior peninsula during the monsoon season with National Aeronautics Space Administration-Modern Era Retrospective-analysis for Research and Applications (NASA-MERRA), pre-monsoon with Japanese 25 years Re Analysis (JRA-25), and post-monsoon with climate forecast system reanalysis (CFSR) reanalysis datasets. Among the reanalysis datasets, European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) shows good comparison followed by CFSR, NASA-MERRA, and JRA-25. Further, for the first time, with high resolution and long-term IMD data, the spatial distribution of trends is estimated using robust regression analysis technique on the annual and seasonal rainfall data with respect to different regions of India. Significant positive and negative trends are noticed in the whole time series of data during the monsoon season. The northeast and west coast of the Indian region shows significant positive trends and negative trends over western Himalayas and north central Indian region.

The Asian summer monsoon: an intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century

The boreal summer Asian monsoon has been evaluated in 25 Coupled Model Intercomparison Project-5 (CMIP5) and 22 CMIP3 GCM simulations of the late twentieth Century. Diagnostics and skill metrics have been calculated to assess the time-mean, climatological annual cycle, interannual variability, and intraseasonal variability. Progress has been made in modeling these aspects of the monsoon, though there is no single model that best represents all of these aspects of the monsoon. The CMIP5 multi-model mean (MMM) is more skillful than the CMIP3 MMM for all diagnostics in terms of the skill of simulating pattern correlations with respect to observations. Additionally, for rainfall/convection the MMM outperforms the individual models for the time mean, the interannual variability of the East Asian monsoon, and intraseasonal variability. The pattern correlation of the time (pentad) of monsoon peak and withdrawal is better simulated than that of monsoon onset. The onset of the monsoon over India is typically too late in the models. The extension of the monsoon over eastern China, Korea, and Japan is underestimated, while it is overestimated over the subtropical western/central Pacific Ocean. The anti-correlation between anomalies of all-India rainfall and Nino3.4 sea surface temperature is overly strong in CMIP3 and typically too weak in CMIP5. For both the ENSO-monsoon teleconnection and the East Asian zonal wind-rainfall teleconnection, the MMM interannual rainfall anomalies are weak compared to observations. Though simulation of intraseasonal variability remains problematic, several models show improved skill at representing the northward propagation of convection and the development of the tilted band of convection that extends from India to the equatorial west Pacific. The MMM also well represents the space–time evolution of intraseasonal outgoing longwave radiation anomalies. Caution is necessary when using GPCP and CMAP rainfall to validate (1) the time-mean rainfall, as there are systematic differences over ocean and land between these two data sets, and (2) the timing of monsoon withdrawal over India, where the smooth southward progression seen in India Meteorological Department data is better realized in CMAP data compared to GPCP data.

Quasi-two-day wave in the lower atmosphere over Hyderabad during summer of 2009

Abstract. The characteristics of the quasi-2-day wave (QTDW) in the lower atmosphere (1–50 km) has been investigated using wind and temperature data obtained from India Meteorological Department (IMD), Hyderabad (17.4° N, 78.5° E) and ECMWF Re-Analysis (ERA-interim) data for (18° N, 78° E) which is the nearest grid to the location of observation. Wind and temperature data of IMD between 15 May and 24 September 2009 in the altitude range of 1–25 km have been used for the present study. Simultaneous ERA data of wind and temperature for the same period between the pressure levels of 1–1000 mb have been analyzed for 2-day wave activity. Appreciable amplitudes of the wave (~2–3 m s−1) could be observed in both IMD and ERA datasets of winds. The thermal amplitude maximum is ~1.5 K in the lower stratosphere (IMD). Amplitudes obtained from ERA data are found to be less than those obtained from IMD measurements. Maximum wind amplitudes (1.5–2 m s−1) are observed in the UTLS region and in the upper stratosphere between 35–45 km. Peak value of temperature oscillation in the upper stratosphere is ~1 K. Wavelet analyses show systematic bursts of QTDW during the summer solstice with a gap of ~5–8 days. The striking similarity between the results of IMD and ERA data can be appreciated. The squared-coherence values between the two datasets in terms of cross-spectral amplitudes are found to be highly significant for 2-day wave in the lower atmosphere.

Impact of 3DVAR assimilation of Doppler Weather Radar wind data and IMD observation for the prediction of a tropical cyclone

This study aims to investigate the impact of the Three-Dimensional Variational (3DVAR) assimilation of Doppler Weather Radar (DWR) wind data together with the India Meteorological Department (IMD) upper air and surface data for the prediction of a tropical cyclone, which formed over the Bay of Bengal. The National Centers for Environmental Prediction Final Analyses (NCEP FNL) data are used to produce initial conditions. Three numerical experiments were designed to study the effect of 3DVAR assimilation. For the first experiment, the model integrations were performed without any assimilation of observations. IMD upper air and surface observations were assimilated using 3DVAR for the second experiment and the third experiment assimilated DWR wind data along with IMD observations. The model results are compared with one another and also with the observations. The results of the study indicate that the assimilation of DWR wind data and IMD data have resulted in improvements in the simulation of strong vertical velocity, higher warm core temperature and strong gradients in the horizontal wind speed as well as improved spatial distribution of the precipitation.