Meteorological Department Research Articles

Bloom Performances during Rainy Season of Off-season Longan (Dimocarpus longan Lour. ‘Phuang Thong’) Grown at Samut Sakhon Province, Thailand

Rainy season is an obstruction for efficient off-season longan production in Thailand. In this experiment, the bloom performances of off-season 'Phuang Thong' longan as affected by microclimate in a private longan orchard at Ban Phaeo district, Samut Sakhon province was evaluated during the rainy season of 2020. This study focused on the reproductive development at four different times (plots) of potassium chlorate application: May, June, July, and August. Air temperature, relative humidity and light intensity at the canopy level were recorded. Rainfall data was obtained from the Meteorological Department of Thailand. The results showed that inflorescence length, female flower number, and sex ratio of female to male of the May plot were the highest compared to other plots, and it was associated with early rainfall occurrence during flower development and blooming time. The August plot had a low growing degree day and a short flowering period, which coincided with frequent rainfall during the flower development period. However, the pollen viability and female flower characters were unaffected by environment. In conclusion, flower induction in the early rainy season, especially in May, was an effective time for the production of off-season ‘Phuang Thong’ longan in Ban Phaeo district, Samut Sakhon province, Thailand. Keywords: Floral biology, Flower, Pollen, Microclimate, Heat unit

Study of Population Dynamics of Insect-pests in Different Growing Environments and their Relationship with Microclimate of Pigeonpea Cultivars

Background: Pigeonpea is a low input, rainfed crop that provides economic returns every part of the plant. Insect-pests are the major biotic constraints limiting the productivity of this crop. Keeping all these factors in mind, the present experiment was conducted to study the population dynamics of insect-pests in different growing environments and their relationship with microclimate of pigeonpea cultivars. Methods: A field experiment was conducted at research farm of Department of Agricultural Meteorology, Chaudhary Charan Singh Haryana Agricultural University, Hisar, during Kharif season 2017. Insect-pests population was recorded from the five tagged plants at weekly interval starting from 38th standard meteorological week (SMW) till harvesting of the crop. Ground sheet method was used to record the population of larvae of M. vitrata and H. armigera. Larvae of M. vitrata and H. armigera falling on cloth sheet of size 60 cm × 60 cm laid near plant stem were counted. Web formed by M. vitrata larvae were visually counted and then removed to count larvae feeding inside the webs. Result: In our experiment, it was found that H. armigera infestation started from 38th SMW in all the varieties and D1 and D2 sown crop while in D3 sown crop it started in 40th SMW. On the other hand the larval population of M. vitrata started from 38th SMW, 39th SMW and 40th SMW in all varieties and D1, D2 and D3 sown crops, respectively where as the formation of webs in all the varieties and D1 sown crop started from 38th SMW while in D2 and D3 sown crops started from 39th SMW and continued till 45th SMW. Mean larval population of H. armigera, M. vitrata and number of webs per plant was found highest in D1 sown crop on variety Manak.

Investigation of the impacts of climate change and rising temperature on food poisoning cases in Malaysia.

This study is an attempt to investigate climate-induced increases in morbidity rates of food poisoning cases. Monthly food poisoning cases, average monthly meteorological data, and population data from 2004 to 2014 were obtained from the Malaysian Ministry of Health, Malaysian Meteorological Department, and Department of Statistics Malaysia, respectively. Poisson generalised linear models were developed to assess the association between climatic parameters and the number of reported food poisoning cases. The findings revealed that the food poisoning incidence in Malaysia during the 11 years study period was 561 cases per 100 000 population for the whole country. Among the cases, females and the ethnic Malays most frequently experienced food poisoning with incidence rates of 313 cases per 100,000 and 438 cases per 100,000 population over the period of 11 years, respectively. Most of the cases occurred within the active age of 13 to 35 years old. Temperature gave a significant impact on the incidence of food poisoning cases in Selangor (95% CI: 1.033-1.479; p = 0.020), Melaka (95% CI: 1.046-2.080; p = 0.027), Kelantan (95% CI: 1.129-1.958; p = 0.005), and Sabah (95% CI: 1.127-2.690; p = 0.012) while rainfall was a protective factor in Terengganu (95% CI: 0.996-0.999; p = 0.034) at lag 0 month. For a 1.0°C increase in temperature, the excess risk of food poisoning in each state can increase up to 74.1%, whereas for every 50 mm increase in rainfall, the risk of getting food poisoning decreased by almost 10%. The study concludes that climate does affect the distribution of food poisoning cases in Selangor, Melaka, Kelantan, Sabah, and Terengganu. Food poisoning cases in other states are not directly associated with temperature but related to monthly trends and seasonality.

Simulation of Track and Landfall Process of Severe Cyclonic Storm Mora over the Bay of Bengal using WRF-ARW Model

The simulations of Severe Cyclonic Storm (SCS) Mora (28-31 May 2017) generated over the Bay of Bengal (BoB) are performed in this study to analyze its features, landfall, and track using the Weather Research and Forecasting (WRF) model. WRF-ARW model has been used on a 10 km Horizontal Resolution (HR) domain for 96, 72, 48, and 24-hour lead time simulations. The model's performance is assessed by examining Mean Sea Level Pressure (MSLP), vertical distribution of velocity components, wind flow pattern, relative vorticity, vertical wind shear, relative humidity, latent heat flux at the surface, and track pattern. The simulated results are compared carefully to the observations from the Bangladesh Meteorological Department (BMD) and the India Meteorological Department (IMD). The findings are reasonably consistent with the observations. The simulated track is also reasonable even up to 72 hours in advance. Finally, the study's results suggest that the WRF model can be used as an effective tool in predicting TCs over the BoB.
 Dhaka Univ. J. Sci. 71(2): 142-152, 2023 (July)

Spatiotemporal rainfall variability and its relationship to flash flood risk in Northeastern Sylhet Haor of Bangladesh

Abstract The variability and distribution of rainfall are uniquely significant for climatic risk prediction. This study aims to assess spatiotemporal rainfall variability and flash flood intensity events in the Sylhet haor region of Bangladesh by analyzing rainfall data from April for the period 1995–2022. For this, we used both Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data and Bangladesh Meteorological Department (BMD) daily rainfall data. Rainfall patterns were studied using zonal statistics in ArcGIS and graphical illustration. The results revealed that the rainfall pattern was erratic and showed a range of spatiotemporal variability. If the average rainfall exceeds 250 mm in Meghalaya and Assam and 400 mm in Sylhet, severe flash floods may occur in the Sylhet haor region. An increase in pre-monsoon rainfall and its shift from May to April may increase the intensity of flash floods and consequently damage the rice crop. This finding might help flood management agencies to develop flood management strategies, prepare flood contingency plans, provide real-time and advanced warnings to strengthen flood warning and forecasting systems, and schedule seasonal agricultural activities.

Exploring the Link Between Insulin-like Growth Factor-1 (IGF-1) and Body Trait Measurements in Prepubertal Goat Kids in a Humid Subtropical Climate

Background: Insulin-like growth factor 1 (IGF-1) is produced throughout life and contributes to the growth of nearly all the organs in the body. Current study investigated the relationship between IGF-1 concentrations with body trait measurements in prepubertal period of male and female Assam Hill Goat kids. Methods: Twenty-four (24) goat kids (12 male + 12 female) were used for the experiment for a period of 7 months (March-September, 2022). Climatic data points were obtained from the State Meteorological Department, Meghalaya and the temperature-humidity index (THI) was calculated. The experimental months was grouped into fourteen (14) periods (Period 1-14) based on the severity of heat stress as indicated by the THI values. Blood samples were collected at every 15 days interval for determination of serum IGF-1 levels and on the same day of collection the phenotypic growth trait measurements were recorded. Result: Results showed that the IGF-1 concentration in the experimental kids significantly (P less than 0.05) increased until Period 6; then a steady non-significant (P greater than 0.05) decline till Period 14. Correlations between IGF-1 concentrations and the phenotypic body trait measurements were significant (P less than 0.05). In conclusion, serum IGF-1 level increases with increase in THI which may be attributed to its thermos-protective effect against environmental stressors.

Drought Assessment and Monitoring for Rajshahi District Using Meteorological Drought Indices

An analysis for drought condition has been made in the Rajshahi district of Bangladesh. The assessment of drought for the country has become an urgent need. The agriculture of the country is dependent on the amount of precipitation. As drought and precipitation are inversely related so it is desired to know the condition of Rajshahi. The main characteristics that define drought are the absence of precipitation for a lengthy period and a low distribution of rainfall in contrast to the normal climatic condition. The monthly maximum average temperature data, monthly minimum average temperature data and total monthly precipitation data for the last 48 years (1971-2019) from Bangladesh Meteorological Department (BMD) are utilized for the analysis process. An online software named DrinC is preferred due to its open-source platform and its user-friendly interface. The meteorological data serve as inputs for the software. The Deciles Index (DI), the Standardized Precipitation Index (SPI) and the Reconnaissance Drought Index (RDI) have been used to identify the drought condition of Rajshahi. The values of the DI and SPI are generated from the precipitation data. However, for the generation of the RDI, the precipitation data as well as the potential evapo-transpiration (PET) data, which is an intermediate data that is generated from the two different types of temperature data via DrinC software are used. The values of SPI, RDI and DI values are presented in a tabular as well as in graphical form. From the comparison of these data and from the study of the recurrence interval the state and the repetition of drought in Rajshahi district can be determined.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Association between rainfall and Escherichia coli in live bivalve molluscs harvested in Sardinia, Italy

Rainfall is generally accepted as one of the most important factors associated with an increased level of E. coli in bivalve molluscs. Performing microbiological risk assessment is relevant to official control authorities to determine the sanitary status of harvesting areas and, therefore, develop monitoring strategies and identify management practices that could be used to improve the quality and safety of the final product. The present study aimed to investigate the impact of rainfall on the content of E. coli in bivalve molluscs farmed in Sardinia (Italy). Enumeration of E. coli was performed according to the Most Probable Number (MPN) method (ISO 16649-3) on 1,920 bivalve samples collected from 7 regional counties between 2018 and 2020. Bivalve molluscs samples included 955 mussels (Mytilus galloprovincialis), 500 oysters (Crassostrea gigas), 325 clams (Ruditapes decussatus), 94 warty venus (Venus verrucosa), and 46 lagoon cockles (Cerastoderma glaucum). Rainfall data were obtained by the Department of Meteorology of the ARPA Sardegna. For each sampling site, GPS coordinates were used to identify gauge stations within catchment areas. Cumulative rain (mm) was recorded 1, 3, 5, 7, and 15 days before sampling, among which the 7-day cumulative rain was the strongest predictor of E. coli counts. Several thresholds of 7-day cumulative rain (from <10 mm up to >300 mm) before sampling were used to estimate the chances of a non-compliant sample (E. coli levels above the limit for sanitary class A; 230 MPN/100 g). The 7-day cumulative rain was positively associated with the chances of non-compliance. When the 7-day cumulative rain before sampling was >300 mm, 80.5 % of the samples were non-compliant, and the odds of a non-compliant sample were 23.6 times higher, as compared to samples harvested when the 7-day cumulative rainfall was <10 mm. Precipitation data could be a useful tool for interpreting anomalous results from official control authorities and reduce the costs that originate from closure of production areas.

Geospatial dataset for hydrologic analyses in India (GHI): a quality-controlled dataset on river gauges, catchment boundaries and hydrometeorological time series

Abstract. Streamflow gauging stations not only track the pulse of rivers but also act as common reference points for hydrologic and other environmental analyses. As such, streamflow data and metadata on gauging stations – Geographic Information System (GIS) data on station locations, their upstream catchment boundaries and river flow networks – are critical for analyses. However, for India's river basins, the availability of such data is limited; when available, data are not in an analysis-ready format and can have substantial errors. Studies often use available information from India's water agencies as is, without checking its validity. This study addresses the above limitations by building a new dataset using existing metadata (from the Central Water Commission, CWC, and the Water Resources Information System, WRIS) and checking it against publicly available information from global data sources (e.g., World Wildlife Fund, Multi-Error-Removed Improved-Terrain Hydro and Copernicus) and online maps (e.g., Google Maps). The quality control process categorizes existing metadata based on their consistency with these sources; also, existing metadata are supplemented with additional information where needed. The new dataset developed here is called the “Geospatial dataset for Hydrologic analyses in India” (GHI) and uses Hydrological data and maps based on Shuttle Elevation Derivatives at multiple Scales (HydroSHEDS) data as the underlying template. GHI has both geospatial and time series information. In this initial version of GHI, the spatial domain includes only the river basins of Peninsular India where daily streamflow data are publicly available. Following the quality control process, the CWC's 645 stations in Peninsular India were categorized into three groups: Group 1 (reliable metadata and adequate daily streamflow data; 213 stations), Group 2 (reliable metadata and inadequate or no daily streamflow data; 259 stations) and Group 3 (missing or unreliable metadata; 173 stations). For each of the 472 stations falling into groups 1 and 2, catchment-specific annual and monthly time series spanning 71 water years (1950–2020) of the following were compiled: observed precipitation from the Indian Meteorological Department (IMD); observed streamflow from WRIS; estimated precipitation, evapotranspiration (ET) and streamflow from ERA5-Land; and ET from the Global Land Evaporation Amsterdam Model (GLEAM). A preliminary analysis of catchment-scale time series of data indicates that, while the compiled data appear reasonable over most of the study domain, spurious runoff–precipitation ratios were observed in the hilly coastal regions of Western India. This adds to yet another data-related obstacle faced by the hydrologic community. In order to quantify historical changes and reconcile them with anticipated future changes, the community needs robust and reliable hydrographic and hydrometeorological datasets as well as unrestricted access to such datasets. The goal of this study is to highlight the limitations of existing datasets and pave the way for a community-led effort towards building the needed datasets. GHI serves as a placeholder until such datasets become available. Potential improvements to GHI are discussed. GHI is publicly available at https://doi.org/10.5281/zenodo.7563599 (Goteti, 2023).

Role of Agrometeorological Advisory Services in Enhancing Food Security and Reducing Vulnerability to Climate Change

Abstract Providing knowledge inputs to farmers is critical to reduce their vulnerability and enhance resilience against climate change. In developing countries such as India, where small holdings and rain-fed agriculture are predominant, knowledge inputs become even more critical. The India Meteorological Department has provided integrated agrometeorological advisory services (AAS) to farmers since 2008. In this paper, we estimate the scale of access to AAS and its impact on crop yields in 1000 households across 10 villages in two agroclimatic zones in India. We find evidence suggesting that access to AAS can have a significant impact on crop yields in the kharif (June–September) season, whereas other inputs are more important in the case of rabi (winter) crops. Specifically, the yields of pigeon pea, soybean, and pearl millet are higher by 233, 98, and 318 kg ha−1, respectively, for AAS beneficiaries. For the entire study area, this translates to a value addition of $9.66 million for these three crops in one season. Our results show that AAS can be an important contributor to meeting the developmental goals of enhancing food security in dry-land agriculture and building resilience against climate change. Significance Statement In the era of climate change, with rapidly increasing weather and climatic variability, protecting the incomes of small farmers and ensuring they have the capacity to adapt and build resilience to the growing impacts of climate change is an urgent necessity. We have studied the impact of knowledge services such as the agrometeorological advisory services of the India Meteorological Department on crop yields for major crops in dry agroclimatic zones in India. The study shows that large public programs like the agrometeorological advisory services that bring science to people in meaningful ways can contribute significantly to meeting developmental goals and building resilience against climate change.

Assessing the impact of temperature and rainfall on mustard yield through detrended production index

The present study was conducted aiming to evaluate the individual and combined impact of temperature and rainfall on mustard yield through detrended production index for five districts of West Bengal viz., Hooghly, Nadia, Burdwan, Mursidabad and South 24 Parganas. The crop data and weather information were collected from various stations of those five locations. The selected study areas belong to different agroclimatic zones of the state, namely old alluvial zone, new alluvial zone and coastal saline zone. Mustard growing season in these districts starts from middle of October and continues upto middle of January (Rabi season). The detailed information on yield for 18 years (1997 to 2014) was collected from Government of West Bengal and weather data were collected from India Meteorological Department. The entire growing season of mustard was divided into vegetative and reproductive stages for convenience of the study. Although a definite trend among them existed. Moreover, when all the five locations are considered, overall increase in the year-wise yield was significant with R2 value 0.63. Some R square had poor values. Higher values of R2 indicated the significance of technological trend in case of Hooghly (R2 = 0.46), Nadia (R2 = 0.65) and South 24 Parganas (R2 = 0.73) districts where as it was not significant for Burdwan and Mursidabad. A gradual decrease in yield was observed with temperature increment from 0.50C to 2.00C. The results indicated a reduction of 0.36%, 0.72%, 1.01% and 1.4% in mustard yield in 0.50C, 10 C, 1.50C and 20C increased temperature condition, respectively. Declined yield of mustard will be 908 kg ha-1 in the study location at 20C more temperature condition. Yield reduction is more if higher temperature coincides with the vegetative stage. Time of sowing should be adjusted so that vegetative stage can escape the high temperature period. But all other required management practices should be performed along with the mentioned one. Otherwise several other biotic and abiotic stresses may lower down the yield too. Thus, the results of this work strongly support the idea of engaging DPI to evaluate the impacts of prime weather parameters on crop production and generate yield forecasting models based on that

Performance of land surface schemes on simulation of land falling tropical cyclones over Bay of Bengal using ARW model

The present study encompasses the performance of Land Surface Model (LSM) physics on simulation of Tropical Cyclones (TCs) key characteristics - track, mean sea level pressure (MSLP), maximum sustained wind (MSW) and rainfall. The impact of four LSM schemes - Thermal Diffusion, Noah, RUC and Noah-MP, is evaluated for the simulation of Severe Cyclonic Storm (SCS) ‘Vardah’ that crossed Tamil Nadu coast, near Chennai on 12 December, 2016 and Extremely Severe Cyclonic Storms (ESCS) ‘Fani’ that crossed Odisha coast, close to Puri on 03 May, 2019. For this purpose, the Advanced Weather Research and Forecasting (ARW) model, configured with a single domain of 9 km horizontal resolution covering the Bay of Bengalis considered. The initial and lateral boundary conditions to the model integration are taken from National Centers for Environmental Prediction (NCEP) Final Analysis (FNL). The model simulated track is verified with India Meteorological Department (IMD) observed track for both the cases. The model simulated MSW and MSLP at the landfall location is validated with IMD best estimation along with fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) Re-analysis (ERA5) products. The rainfall associated with both the cyclones are compared with ERA5 and Global Precipitation Measurement (GPM) rainfall for its validation. The track of TCs Vardah and Fani are well simulated with all the four land surface schemes with reasonable accuracy in landfall position and time of landfall of the systems. The Along Track Error (ATE) and Cross Track Error (CTE) are minimal for the unified Noah LSM scheme. The landfall position error (about 2 km only) is significantly improved with the unified Noah scheme. In case of rainfall forecast, LSMs tend to overestimate the rainfall during landfall of both systems. It is also noticed that overestimation is more towards inland than on the coast. Out of all four LSMs, rainfall estimation from the RUC is closest to the GPM and ERA5 rainfall estimates during landfall. In addition to this, RUC scheme intensifies the cyclones in terms of MSLP and MSW during the landfall of the system as compared to the other parameterization schemes.

Quantifying Trends of Historical Climatic Variables in the Jessore Region of Bangladesh

The aim of the study was to detect trends in salient key climate variables in the Jessore region of Bangladesh for the years 1985–2014, and the data was collected from the Bangladesh Meteorological Department (BMD). Annual rainfall, annual maximum temperature, and annual cloud coverage were analyzed using the non-parametric Mann-Kendall test, linear regression, and LOWESS curve to detect trends in the series. The rainfall and cloud coverage showed a decline trend at a rate of (4.50 mm/year; 45.02 mm/decade) and (0.045 octas/year; 0.45829 oktas/decade), respectively, whereas temperature manifested an increment trend (0.0285°C/year; 0.2854°C/decade), where cloud coverage was the only significant variable. The structural breakdown point was found using the graphical method and the Chow test, which showed in 2004 that both the series containing breakdown points and cloud coverage exhibit statistical significance.

Impacts of climate change on drought and its consequences on the agricultural crop under worst-case scenario over the Godavari River Basin, India

Extreme dry and hot conditions lead to intense droughts in central India. However, the precise changes in future drought characteristics and their consequences on crop production have not been fully examined in Godavari River Basin. Therefore, this study focuses on the spatiotemporal modelling of climate change impacts on drought over the past, present, and future periods using long-term high spatial gridded data. The past and present observed datasets were obtained from Indian Meteorological Department, whereas the future datasets were obtained from d4PDF Scenarios. The standardized precipitation evapotranspiration index (SPEI) was applied to analyse drought statistics to identify the worst-case drought scenario (i.e. year most affected by severe-to-extreme drought with high drought severity), which was used further to access precise drought risk. Because SPEI considers the effect of both rainfall and temperature and therefore it is the widely recommended meteorological drought indices and best suited for climate change impact assessment. Drought severity was analysed using a Multi-criteria assessment risk matrix, and the areas under higher agriculture drought risk were mapped. The frequency of droughts with higher intensity and magnitude increased in the present decade compared to the past three decades. 75% area showed higher vulnerability to drought risks, whereas only 25% showed lower risks and less vulnerability to drought occurrences. Results demonstrate that drought severity varies from one sub-basin to another. More drought episodes were predicted for the central belt, particularly over the sub-basins of Wardha, Wainganga, Pranitha, and part of Indravati and Lower Godavari. 11% of the area covering six districts in the GRB was identified as highly vulnerable, and rice production was drastically reduced, accounting for 41.02% of production loss during the worst-case drought event. The frequency of severe-to-extreme droughts is expected to increase under future scenarios; therefore, effective mitigation strategies are recommended to minimise the agriculture drought risks and economic loss.

An analytical study of the climatic elements in Bazian region - Sulaymaniyah for the period from 1985 - 2022 and its impact on the planting dates of some crops

During a field study in Bazian Plain which is one of the most important agricultural areas in Sulaymaniyah it became clear that current scenario is linked to rapid climate change directly. Arid and semi-arid ecosystems are likely to be particularly affected by changes in wind speed and vapour pressure regimes, changes in those regimes may show from climate data that vapour pressure has clearly increased during these years. For instance, in January and February of 2017 wind speed reached 8 and 9 (m. sec.-1). Sulaymaniyah Meteorology Department reported a lesser speed of 0.1 (m. sec.-1) in the months 4, 5, 6 and 9, on average the greatest rates of 3.1 and 2.2 (m. sec.-1) in 1985 and 1992. on the other hand, in 1991 recorded the lowest rate of (1 m. sec.-1) amounts and times of precipitation during the study years, floods occurred in some seasons the dry seasons less than 300 (ml) were very few, while the remaining seasons were more than 450 (ml) rainfall amounts in the seasons 2006-2007, 2007-2008 and 2008-2009 were among the driest seasons during the years of the study Which was straight (66000, 87000, 86000) dunam. as the agricultural areas were reduced to their lowest levels, although the levels of precipitation were acceptable. The data gathered found that the average annual temperature rate has increased by over 1.3(°C) with the warmest year for this time being recorded in 2010 at 21.55(°C). Sunshine was the lowest in terms of the number of daylight hours in December with total annual averages for the period 1985-2022 for December being 184.4 (hours) 1996 was the lowest with 2.3 (hr.) and 2018 was also the second lowest with a value of 3.3 (hr.) During the decades of the study period, wheat growing seasons advanced mostly to November, and in recent decades (2011-2020) (2010-2009) 20 farmers plant in December to ensure rainfall.

Estimating Daily Temperatures over Andhra Pradesh, India, Using Artificial Neural Networks

In the recent past, Andhra Pradesh (AP) has experienced increasing trends in surface air mean temperature (SAT at a height of 2 m) because of climate change. In this paper, we attempt to estimate the SAT using the GFDL-ESM2G (Geophysical Fluid Dynamics Laboratory Earth System Model version 2G), available from the Coupled Model Intercomparison Project Phase-5 (CMIP5). This model has a mismatch with the India Meteorological Department (IMD)’s observations during April and May, which are the most heat-prone months in the state. Hence, in addition to the SAT from the model, the present paper considers other parameters, such as mean sea level pressure, surface winds, surface relative humidity, and surface solar radiation downwards, that have influenced the SAT. Since all five meteorological parameters from the GFDL-ESM2G model influence the IMD’s SAT, an artificial neural network (ANN) technique has been used to predict the SAT using the above five meteorological parameters as predictors (input) and the IMD’s SAT as the predictand (output). The model was developed using 1981–2020 data with different time lags, and results were tested for 2021 and 2022 in addition to the random testing conducted for 1981–2020. The statistical parameters between the IMD observations and the ANN estimations using GFDL-ESM2G predictions as input confirm that the SAT can be estimated accurately as described in the analysis section. The analysis conducted for different regions of AP reveals that the diurnal variations of SAT in the IMD observations and the ANN predictions over three regions (North, Central, and South AP) and overall AP compare well, with root mean square error varying between 0.97 °C and 1.33 °C. Thus, the SAT predictions provided in the GFDL-ESM2G model simulations could be improved statistically by using the ANN technique over the AP region.

Calibrated multi‐model ensemble seasonal prediction of Bangladesh summer monsoon rainfall

AbstractBangladesh summer monsoon rainfall (BSMR), typically from June through September (JJAS), represents the main source of water for multiple sectors. However, its high spatial and interannual variability makes the seasonal prediction of BSMR crucial for building resilience to natural disasters and for food security in a climate‐risk‐prone country. This study describes the development and implementation of an objective system for the seasonal forecasting of BSMR, recently adopted by the Bangladesh Meteorological Department (BMD). The approach is based on the use of a calibrated multi‐model ensemble (CMME) of seven state‐of‐the‐art general circulation models (GCMs) from the North American Multi‐Model Ensemble project. The lead‐1 (initial conditions of May for forecasting JJAS total rainfall) hindcasts (spanning 1982–2010) and forecasts (spanning 2011–2018) of seasonal total rainfall for the JJAS season from these seven GCMs were used. A canonical correlation analysis (CCA) regression is used to calibrate the raw GCMs outputs against observations, which are then combined with equal weight to generate final CMME predictions. Results show, compared to individual calibrated GCMs and uncalibrated MME, that the CCA‐based calibration generates significant improvements over individual raw GCM in terms of the magnitude of systematic errors, Spearman's correlation coefficients, and generalised discrimination scores over most of Bangladesh areas, especially in the northern part of the country. Since October 2019, the BMD has been issuing real‐time seasonal rainfall forecasts using this new forecast system.

Examination of long-term climatic and fish-catch indicators in India at a macro scale

The quantification of trends and attributes helps in better understanding of the long-term changes in the marine ecosystem. Marine fish-catch was analysed using the statistical data provided by the Central Marine fisheries Research Institute, India. These datasets are used internationally to assess changes in marine fisheries along the coasts of India. In addition the data from the Indian Meteorological Department were used to derive temporal trends of cyclonic indicators such as the Frequency and, Intensity of cyclones. Trend analysis of fishery-based data indicated a significant rise in fish-catch across the NIO basin. The sea surface temperature has reported a significant rise in the past 2 decades, reaching peaks during the months from March to October. There is a reduction in the overall frequency of cyclonic events, with a significant increase in extreme cyclonic events. The normal distribution curve also reports a shift towards a positive direction in the case of SST and fish-catch, while the negative shift is obtained for the frequency of cyclones as well as the intensity of depression between the time-period of 1950-99 to 2000-18. The influence of global warming induced climate change can be observed within these trends.

Active and break spells of summer monsoon over Bangladesh

This study examines the intraseasonal variability of the southwest summer monsoon over Bangladesh using rainfall data from rain gauge stations of the Bangladesh Meteorological Department (BMD) collected over 30 years (1988–2017). In this paper, active and break spells are defined as periods during the peak monsoon months of July and August, in which the daily precipitation lasts for three or more days at 0.5 above and below the daily climatological cycle. The active and break phases of the monsoon over a period of 10 years (2008–2017) were also analyzed by The Weather Research and Forecasting (WRF) model simulation for analysing synoptic conditions. The model simulations for each year were done for the period of 1 May to 30 September with 1 May as the initial condition with a single domain of 30 km resolution and 19 vertical levels. The final operational global analysis data from the Global Forecasting System of National Centers for Environment Prediction (NCEP-FNL) with resolution 1° × 1° is used for model simulation. The model-simulated daily rainfall, Sea Level Pressure (SLP), wind pattern at 850 hPa, 200 hPa, and Outgoing Longwave Radiation (OLR) are compared with the observations from Tropical Rainfall Measuring Mission (TRMM), ERA5 (ERA5 is the European Centre for Medium-Range Weather Forecasts fifth-gen global atmospheric reanalysis data), and Kalpana-1. This study also finds that the increase in rainfall is concurrent with a southwesterly wind and the decrease of rainfall simultaneously occurs with a southeasterly wind. Active days were found to have lower OLR values and lower SLP than break days.