Samir Pokhrel

Unusual Central Indian Drought of Summer Monsoon 2008: Role of Southern Tropical Indian Ocean Warming

Abstract While many of the previous positive Indian Ocean dipole (IOD) years were associated with above (below)-normal monsoon rainfall over central (southern) India during summer monsoon months [June–September (JJAS)], the IOD event in 2008 is associated with below (above)-normal rainfall in many parts of central (southern peninsular) India. Because understanding such regional organization is a key for success in regional prediction, using different datasets and atmospheric model simulations, the reasons for this abnormal behavior of the monsoon in 2008 are explored. Compared to normal positive IOD events, sea surface temperature (SST) and rainfall in the southern tropical Indian Ocean (STIO) in JJAS 2008 were abnormally high. Downwelling Rossby waves and oceanic heat advection played an important role in warming SST abnormally in the STIO. It was also found that the combined influence of a linear warming trend in the tropical Indian Ocean and warming associated with the IOD have resulted in abnormal war...

Why is Indian Ocean warming consistently

Observations have shown that the Indian Ocean is consistently warming and its warm pool is expanding, particularly in the recent decades. This paper attempts to investigate the reason behind these observations. Under global warming scenario, it is expected that the greenhouse gas induced changes in air–sea fluxes will enhance the warming. Surprisingly, it is found that the net surface heat fluxes over Indian Ocean warm pool (IOWP) region alone cannot explain the consistent warming. The warm pool area anomaly of IOWP is strongly correlated with the sea surface height anomaly, suggesting an important role played by the ocean advection processes in warming and expansion of IOWP. The structure of lead/lag correlations further suggests that Oceanic Rossby waves might be involved in the warming. Using heat budget analysis of several Ocean data assimilation products, it is shown that the net surface heat flux (advection) alone tends to cool (warm) the Ocean. Based on above observations, we propose an ocean-atmosphere coupled positive feedback mechanism for explaining the consistent warming and expansion of IOWP. Warming over IOWP induces an enhancement of convection in central equatorial Indian ocean, which causes anomalous easterlies along the equator. Anomalous easterlies in turn excite frequent Indian ocean Dipole events and cause anti-cyclonic wind stress curl in south-east and north-east equatorial Indian ocean. The anomalous wind stress curl triggers anomalous downwelling oceanic Rossby waves, thereby deepening the thermocline and resulting in advection of warm waters towards western Indian ocean. This acts as a positive feedback and results in more warming and westward expansion of IOWP.

Influence of Eurasian snow on Indian summer monsoon in NCEP CFSv2 freerun

The latest version of the state-of-the-art global land–atmosphere–ocean coupled climate forecast system of NCEP has shown considerable improvement in various aspects of the Indian summer monsoon. However, climatological mean dry bias over the Indian sub-continent is further increased as compared to the previous version. Here we have attempted to link this dry bias with climatological mean bias in the Eurasian winter/spring snow, which is one of the important predictors of the Indian summer monsoon rainfall (ISMR). Simulation of interannual variability of the Eurasian snow and its teleconnection with the ISMR are quite reasonable in the model. Using composite analysis it is shown that a positive snow anomaly, which is comparable to the systematic bias in the model, results into significant decrease in the summer monsoon rainfall over the central India and part of the Equatorial Indian Ocean. Decrease in the summer monsoon rainfall is also found to be linked with weaker northward propagation of intraseasonal oscillation (ISO). A barotropic stationary wave triggered by positive snow anomaly over west Eurasia weakens the upper level monsoon circulation, which in turn reduces the zonal wind shear and hence, weakens the northward propagation of summer monsoon ISOs. A sensitivity experiment by reducing snow fall over Eurasian region causes decrease in winter and spring snow depth, which in turn leads to decrease in Indian summer monsoon rainfall. Results from the sensitivity experiment corroborate with those of composite analysis based on long free run. This study suggests that further improvements in the snow parametrization schemes as well as Arctic sea ice are needed to reduce the Eurasian snow bias during winter/spring, which may reduce the dry bias over Indian sub-continent and hence predictability aspect of the model.

Anomalous low tropospheric column ozone over Eastern India during the severe drought event of monsoon 2002: a case study

Background, aim, and scopeThe present study is an attempt to examine some of the probable causes of the unusually low tropospheric column ozone observed over eastern India during the exceptional drought event in July 2002.MethodWe examined horizontal wind and omega (vertical velocity) anomalies over the Indian region to understand the large-scale dynamical processes which prevailed in July 2002. We also examined anomalies in tropospheric carbon monoxide (CO), an important ozone precursor, and observed low CO mixing ratio in the free troposphere in 2002 over eastern India.Results and discussionIt was found that instead of a normal large-scale ascent, the air was descending in the middle and lower troposphere over a vast part of India. This configuration was apparently responsible for the less convective upwelling of precursors and likely caused less photochemical ozone formation in the free troposphere over eastern India in July 2002.ConclusionThe insight gained from this case study will hopefully provide a better understanding of the process controlling the distribution of the tropospheric ozone over the Indian region.

Assessment of water quality using multivariate statistical techniques in the coastal region of Visakhapatnam, India

The present study was intended to develop a Water Quality Index (WQI) for the coastal water of Visakhapatnam, India from multiple measured water quality parameters using different multivariate statistical techniques. Cluster analysis was used to classify the data set into three major groups based on similar water quality characteristics. Discriminant analysis was used to generate a discriminant function for developing a WQI. Discriminant analysis gave the best result for analyzing the seasonal variation of water quality. It helped in data reduction and found the most discriminant parameters responsible for seasonal variation of water quality. Coastal water was classified into good, average, and poor quality considering WQI and the nutrient load. The predictive capacity of WQI was proved with random samples taken from coastal areas. High concentration of ammonia in surface water during winter was attributed to nitrogen fixation by the phytoplankton bloom which resulted due to East India Coastal Current. This study brings out the fact that water quality in the coastal region not only depends on the discharge from different pollution sources but also on the presence of different current patterns. It also illustrates the usefulness of WQI for analyzing the complex nutrient data for assessing the coastal water and identifying different pollution sources, considering reasons for seasonal variation of water quality.

Rain rate measurements over global oceans from IRS-P4 MSMR

In this paper rain estimation capability of MSMR is explored. MSMR brightness temperature data of six channels corresponding to three frequencies of 10, 18 and 21 GHz are colocated with the TRMM Microwave Imager (TMI) derived rain rates to find a new empirical algorithm for rain rate by multiple regression. Multiple correlation analysis involving various combinations of channels in linear and non-linear forms and rain rate from TMI is carried out, and thus the best possible algorithm for rain rate measurement was identified which involved V and H polarized brightness temperature measurements at 10 and 18 GHz channels. This algorithm explained about 82 per cent correlation (r) with rain rate, and 1.61 mm h-1 of error of estimation.Further, this algorithm is used for generating global average rain rate map for two contrasting months of August (2000) and January (2001) of northern and southern hemispheric summers, respectively. MSMR derived monthly averaged rain rates are compared with similar estimates from TRMM Precipitation Radar (PR), and it was found that MSMR derived rain rates match well, quantitatively and qualitatively, with that from PR.

Indian summer monsoon precipitating clouds: role of microphysical process rates

The budget analysis of microphysical process rates based on Modern Era Retrospective-analysis for Research and Applications (MERRA) products are presented in the study. The relative importance of different microphysical process rates, which is crucial for GCMs, is investigated. The autoconversion and accretion processes are found to be vital for Indian Summer Monsoon (ISM). The map-to-map correlations are examined between observed precipitation and MERRA reanalysis. The pattern correlations connote the fidelity of the MERRA datasets used here. Results of other microphysical parameters (e.g. ice water content from CloudSat, high cloud fraction from CALIPSO and MODIS, latent heating from TRMM, cloud ice mixing ratio from MERRA) are presented in this study. The tropospheric temperature from reanalysis product of MERRA and NCEP are also analyzed. Furthermore, the linkages between cloud microphysics production rates and dynamics, which are important for North–South tropospheric temperature gradient for maintaining the ISM circulation, are also discussed. The study demonstrates the microphysical process rates, which are actually responsible for the cloud hydrometeors and precipitation formation on the monsoon intraseasonal oscillations timescale. Cloud to rain water auto-conversion and snow accretion rates are the dominant processes followed by the rain accretion. All these tendency terms replicates the similar spatial patterns as that of precipitation. The quantification of microphysical process rates and precipitation over different regions are shown here. The freezing rate is also imperative for the formation of cloud ice as revealed by the observation. Freezing rates at upper level and snow accretion at middle level may have effect on latent heating release. Further it can modulate the north–south temperature gradient which can influence the large-scale monsoon dynamics. The rain water evaporation is also considered as a key aspect for controlling the low level moisture convergence (source of water vapor) in ISM. This study has highlighted the importance of detailed microphysical production rates for warm and mixed-phase cloud processes, which is a major source of uncertainty in the climate models. Better understanding of these processes will definitely add value to the present generation climate models. Therefore the hypothesis/pathway emerged from the present study may be helpful for the future model development research.

Influence of preonset land atmospheric conditions on the Indian summer monsoon rainfall variability

A possible link between preonset land atmospheric conditions and the Indian summer monsoon rainfall (ISMR) is explored. It is shown that, the preonset positive (negative) rainfall anomaly over northwest India, Pakistan, Afghanistan, and Iran is associated with decrease (increase) in ISMR, primarily in the months of June and July, which in turn affects the seasonal mean. ISMR in the months of June and July is also strongly linked with the preonset 2 m air temperature over the same regions. The preonset rainfall/2 m air temperature variability is linked with stationary Rossby wave response, which is clearly evident in the wave activity flux diagnostics. As the predictability of Indian summer monsoon relies mainly on the El Nino–Southern Oscillation (ENSO), the found link may further enhance our ability to predict the monsoon, particularly during a non-ENSO year.

Dynamical features of incessant heavy rainfall event of June 2013 over Uttarakhand, India

Abstract The southward penetration of mid-latitude westerlies and their interaction with monsoon current are harbinger of intense rainfall activity over northern and central India. Such synoptic condition prevailed during June 14–17, 2013, and unleashed relentless rainfall over the state of Uttarakhand. Observational aspects of this event have been explored using surface, satellite and reanalysis data. Precipitation features have been explored using data from Precipitation Radar Onboard TRMM satellite in conjunction with TRMM-TB42, Automatic Weather Station and Automatic Rain Gauge Station data. The ERA interim dataset has been used to explore prevalent synoptic conditions, and Modern-Era Retrospective Analysis for Research and Applications reanalysis fields revealed that prevalent synoptic features led to moisture flux convergence in lower troposphere. Vertically integrated water vapor transport over the India and adjoining oceanic region is evaluated to gain an insight into the dynamical mechanism of rainfall activity over Uttarakhand. Results reveal that interplay between movement of monsoon low along the monsoon trough resulting in strong low-level convergence and constant feeding of moisture from Arabian Sea and Bay of Bengal and strong upper-level divergence owing to southward intrusion of mid-latitude westerly trough resulted in heavy rainfall activity over Uttarakhand.

An empirical algorithm for cloud liquid water from MSMR and its utilization in rain identification

In this paper, an empirical method to estimate cloud liquid water from Indian Remote Sensing P4 (IRS-P4) Multi-frequency Scanning Microwave Radiometer (MSMR) measurements is presented. MSMR brightness temperatures are collocated with concurrent observations of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)-derived cloud liquid water. The multiple-correlation coefficient between TMI-derived cloud liquid water and logarithmic of MSMR-derived brightness temperatures, and their differences at 18- and 21-GHz channels, is found to be about 82.4%. The relationship thus obtained has an rms error of 8.75 mgcm/sup -2/ in the measurements of cloud liquid water from MSMR with respect to TMI measurements. Verification of the algorithm is carried out with another set of concurrent measurements from MSMR and TMI. Further, the MSMR-derived cloud liquid water over the global oceans and for extreme weather conditions (cyclone) are compared with that from TMI and the Special Sensor Microwave/Imager (SSM/I) for independent verification. The cloud liquid water from MSMR is further used to successfully delineate rain events for quantitative estimation of rain rate from MSMR.