Cloudburst events observed over Uttarakhand during monsoon season 2017 and their analysis

Cloudburst is an extreme weather event characterised by the occurrence of a large amount of rainfall over a small area within a short span of time with a rainfall of 100 mm or more in one hour. It is responsible for flash flood, inundation of low lying areas and landslides in hills causing extensive damages to life and property. During monsoon season 2017 five number of cloudburst events are observed over Uttarakhand and analysed. Self Recording Rain Gauge (SRRG) and 15 minutes interval data from the newly installed General Packet Radio Service (GPRS) based Automatic Weather Station (AWS) are able to capture the cloudburst events over some areas in Uttarakhand. In this paper, an attempt has been made to find out the significant synoptic and thermodynamic conditions associated with the occurrence of the cloudburst events in Uttarakhand. These 5 cases of cloudburst events that are captured during the month of June, July and August 2017 in Uttarakhand are studied in detail. Synoptically, it is observed that the existence of trough at mean sea level from Punjab to head Bay of Bengal running close to Uttarakhand, the movement of Western Disturbance over north Pakistan and adjoining Jammu & Kashmir and existence of cyclonic circulation over north Rajasthan and neighbourhood are favourable conditions. Also, the presence of strong south-westerly wind flow from the Arabian Sea across West Rajasthan and Haryana on upper air charts are found during these events. Thermodynamically, the Convective Available Potential Energy (CAPE) is found to be high (more than 1100 J/Kg) during most of the cases and vertically integrated precipitable water content (PWC) is more than 55mm. The GPRS based AWS system can help in prediction of the cloud burst event over the specified location with a lead time upto half to one hour in association with radar products.

Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

Influence of meteorological parameters on wheat yield under different sowing conditions

Field experiments were conducted on the research farm of IARI, New Delhi during Rabi 2016-17 and 2017-18. Three varieties of wheat (PBW-723, HD-2967 and HD-3086) were sown on three different dates for generating different weather condition during various phenological stages of crop. Results showed that during early crop growth stages soil moisture had higher value and soil temperature had lower value and with progress of crop growth stage, the moisture in the upper layer decreased and soil temperature increased significantly as compared to the bottom layers. During tillering and jointing stage, air temperature within canopy was more and relative humidity was less while during flowering and grain filling stage, air temperature within canopy was less and relative humidity was more in timely sown crop as compared to late and very late sown crop. Radiation use efficiency and relative leaf water content had significantly higher value while leaf water potential had lower value in timely sown crop followed by late and very late sown crop. Yield had higher value in HD-3086 followed by HD-2967 and PBW-723 in all weather conditions. Canopy air temperature difference had positive value in very late sown crop particularly during flowering and grain-filling stages. This reflects in the yield. Yield was more in timely sown crop as compared to late and very late sown crop.

Nationwide CoViD-19 lockdown impact on air quality in India

This paper discusses the comparative results of surface and satellite measurements made during the Phase1 (25 March to 14 April), Phase2 (15 April to 3 May) and Phase3 (3 May to 17May) of Covid-19 imposed lockdown periods of 2020 and those of the same locations and periods during 2019 over India. These comparative analyses are performed for Indian states and Tier 1 megacities where economic activities have been severely affected with the nationwide lockdown. The focus is on changes in the surface concentration of sulfur dioxide (SO2), carbon monoxide (CO), PM2.5 and PM10, Ozone (O3), Nitrogen dioxide (NO2) and retrieved columnar NO2 from TROPOMI and Aerosol Optical Depth (AOD) from MODIS satellite. Surface concentrations of PM2.5 were reduced by 30.59%, 31.64% and 37.06%, PM10 by 40.64%, 44.95% and 46.58%, SO2 by 16.73%, 12.13% and 6.71%, columnar NO2 by 46.34%, 45.82% and 39.58% and CO by 45.08%, 41.51% and 60.45% during lockdown periods of Phase1, Phase2 and Phase3 respectively as compared to those of 2019 periods over India. During 1st phase of lockdown, model simulated PM2.5 shows overestimations to those of observed PM2.5 mass concentrations. The model underestimates the PM2.5 to those of without reduction before lockdown and 1st phase of lockdown periods. The reduction in emissions of PM2.5, PM10, CO and columnar NO2 are discussed with the surface transportation mobility maps during the study periods. Reduction in the emissions based on the observed reduction in the surface mobility data, the model showed excellent skills in capturing the observed PM2.5 concentrations. Nevertheless, during the 1st & 3rd phases of lockdown periods AOD reduced by 5 to 40%. Surface O3 was increased by 1.52% and 5.91% during 1st and 3rd Phases of lockdown periods respectively, while decreased by -8.29% during 2nd Phase of lockdown period.

Analysis of rainfall trends over Tripura

Rainfall in India has very high temporal and spatial variability. The rainfall variability affects the livelihood and food habits of people from different regions. In this study, the rainfall trends in two stations in the north-eastern state of Tripura, namely Agartala and Kailashahar have been studied for the period 1955-2017. The state experiences an annual mean of more than 2000 mm of rainfall, out of which, about 60% occurs during the monsoon season and about 30% in pre-monsoon. An attempt has been made to analyze the trends in seasonal and annual rainfall, rainy days and heavy rainfall in the two stations, during the same period.Non-parametric Mann-Kendall test has been used to find out the significance of these trends. Both increasing and decreasing trends are observed over the two stations. Increasing trends in rainfall, rainy days and heavy rainfall are found at Agartala during pre-monsoon season and decreasing trends in all other seasons and at annual scale. At Kailashahar, rainfall amount (rainy days & heavy rainfall) is found to be increasing during pre-monsoon and monsoon seasons (pre-monsoon season). At annual scale also, rainfall and rainy days show increasing trends at Kailashahar. The parameters are showing decreasing trends during all other seasons at the station. Rainy days over Agartala show a significantly decreasing trend in monsoon, whereas no other trend is found to be significant over both the stations.

Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

Variability of CPT and tropopause characteristics over the Arabian Peninsula

. The climatology of the cold-point tropopause (CPT) and tropopause characteristics in a subtropical area like The Arabian Peninsula is examined using the radiosonde data of the CPT characteristics and NCEP Reanalysis data of the tropopause characteristics. The monthly mean data for January and July are analyzed for three stations, namely Medina, Tabuk and Dammam in Saudi Arabia. The trends of CPT and tropopause characteristics of pressure, height, temperature, temperature anomalies, relative humidity, wind speed and potential temperature are also analyzed. The trends of these characteristics show that they experienced a sharp change during the 1990s and a significant change for the period from 2000 to 2016. For the whole period of study, the month of July, CPT and tropopause pressure decreased for about 5 hPa, whereas the height increased for more than 100 m. The temperature experienced a sudden drop during the beginning of the 1990s and a smooth decrease during the following years in January. Furthermore, a strong correlation is found between the CPT temperature and the Solar Cycle during the ‘90s period then it decreased sharply after this period.

Extent of diurnal cycle of rainfall and its intra seasonal variation over coastal Tamil Nadu during north east monsoon season

The diurnal variation of north east monsoon rainfall of coastal Tamil Nadu represented by four coastal stations Chennai Nungambakkam (Nbk), Chennai Meenambakkam (Mbk), Nagapattinam (Npt) and Pamban (Pbn) was studied in detail based on hourly rainfall data of rainy days only, for the period 1 Oct-31 Dec for the 47/48 year period 1969-2016/2017. Mean Octet rainfall and its anomaly were computed for the 8 octets 00-03,…., 21-24 hrs of the day and the anomaly was tested for statistical significance. Various analysis for the individual months of Oct, Nov, Dec and the entire period Oct-Dec were separately conducted. The basic technique of evolutionary histogram analysis supplemented by harmonic analysis of octet mean rainfall anomaly was used to detect the diurnal cycle signal. Two indices named as diurnal variation of rainfall index and coefficient of mean absolute octet rainfall anomaly representing the intensity of diurnal variation in dimensionless numbers were defined, computed and interpreted. The analysis based on the above techniques revealed that the diurnal signal which shows an early morning maximum and late afternoon minimum of octet rainfall is well defined in Oct, decreases in Nov and further decreases in Dec for all the 4 stations. Though the diurnal variation manifests a well defined pattern in Dec the signal is not statistically significant in most cases. For Nbk and Mbk there is a weak secondary peak of octet rainfall anomaly occurring in the forenoon and afternoon respectively in Oct and Dec suggesting the presence of semi-diurnal variation of rainfall. Stationwise, the diurnal signal is most well defined for each month/season in Pbn followed by Npt, Nbk and then Mbk. The physical causes behind the diurnal signal and its decrease as the north east monsoon season advances from Oct to Dec have been deliberated. The well known feature of nocturnal maximum of oceanic convection influencing a coastal station with maritime climate and the higher saturation at the lower levels of the upper atmosphere in the early morning hours have been advanced as some of the causes. For the much more complex feature of decrease of diurnal signal with the advancement of the season, the decrease of minimum surface temperature over coastal Tamil Nadu from Oct to Dec causing an early morning conceptual land breeze has been shown as one of the plausible causes based on analysis of temperature and wind. Scope for further work based on data from automatic weather stations, weather satellites and Doppler Weather Radars has been discussed.

Effect of projected climate scenarios on the yields of potato crop and agronomic adaptation options as evaluated by crop growth model

Potato is the most important non-cereal crop in the world and the most prominent winter season crop in India. Growth and yield of potato crop is very much sensitive to higher temperatures and the moisture stress. Hence, the anticipated increase of temperature due to global warming and climatic variability will have anadverse impact on potato production. Keeping this in view, a research work was carried out with the objectives to assess the impact of climate change on potato production and evaluating agronomic adaptation options through a crop growth simulation model (CGSM). Field experiments were carried out to prepare the minimum dataset for calibration and validation of one CGSM, namely InfoCrop. After validation, the model was used to predict the future tuber yield of ten selected stations situated under different agroclimatic regions of the State. In the future scenario 2050, the simulated yield for mid November planted crop likely to be about 11% less than the present level of mean yield. If the crop is planted in December, the percentage of yield reduction may be around 25%.The projected yield reduction, for the stations of higher latitude, is found to be negligible. Three possible agronomic adaptation options, viz., adjustment of date of planting, increase of seed rate and varying sprout length of seed tubers, have been tried as adaptation strategies to combat the adverse effects of climate change. It is concluded that the mid-November planting and longer sprout length will be the best adaptation option. However, the enhanced seed rate is not a viable adaptation option.