A novel weather parameters prediction scheme and their effects on crops

Weather forecast is significantly imperative in today’s smart technological world. A precise forecast model entails a plentiful data in order to attain the most accurate predictions. However, a forecast of future rainfall from historical data samples has always been challenging and key area of research. Hence, in modern weather forecasting a combo of computer models, observation, and knowledge of trends and patterns are introduced. This research work has presented a fitness function based adaptive artificial neural network scheme in order to forecast rainfall and temperature for upcoming decade (2021-2030) using historical weather data of 20 different districts of Karnataka state. Furthermore, effects of these forecasted weather parameters are realized over five major crops of Karnataka namely rice, wheat, jowar, maize, and ragi with the intention of evaluation for efficient crop management in terms of the passing relevant messages to the farmers and alternate measures such as suggesting other geographical locations to grow the same crop or growing other suitable crops at same geographical location. A graphical user interface (GUI) application has been developed for the proposed work in order to ease out the flow of work.

Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products

Accurate weather forecast information has the potential to improve water resources management, energy, and agriculture. This study evaluates the accuracy of medium-range (1–15 d) precipitation forecasts from the Global Forecast System (GFS) over watersheds of eight major dams (Selingue Dam, Markala Dam, Goronyo Dam, Bakolori Dam, Kainji Dam, Jebba Dam, Dadin Kowa Dam, and Lagdo Dam) in the Niger river basin using NASA's Integrated Multi-satellitE Retrievals (IMERG) Final Run merged satellite gauge rainfall observations. The results indicate that the accuracy of GFS forecast varies depending on climatic regime, lead time, accumulation timescale, and spatial scale. The GFS forecast has large overestimation bias in the Guinea region of the basin (wet climatic regime), moderate overestimation bias in the Savannah region (moderately wet climatic regime), but has no bias in the Sahel region (dry climate). Averaging the forecasts at coarser spatial scales leads to increased forecast accuracy. For daily rainfall forecasts, the performance of GFS is very low for almost all watersheds, except for Markala and Kainji dams, both of which have much larger watershed areas compared to the other watersheds. Averaging the forecasts at longer timescales also leads to increased forecast accuracy. The GFS forecasts, at 15 d accumulation timescale, have better performance but tend to overestimate high rain rates. Additionally, the performance assessment of two other satellite products was conducted using IMERG Final estimates as reference. The Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) merged satellite gauge product has similar rainfall characteristics to IMERG Final, indicating the robustness of IMERG Final. The IMERG Early Run satellite-only rainfall product is biased in the dry Sahel region; however, in the wet Guinea and Savannah regions, IMERG Early Run outperforms GFS in terms of bias.

The Storage of Antecedent Precipitation and Air Temperature Signals in Soil Temperature over China

Soil temperature (ST) is one of the key variables in land-atmosphere interactions. The response of ST to atmospheric changes and subsequent influence of ST on atmosphere can be recognized as the processes of signals propagation. Understanding the storing and releasing of atmosphere signals in ST favors the improvement of climate prediction and weather forecast. However the current understanding of the lagging response of ST to atmospheric changes is very insufficient. The analysis based on observation shows that both the storage of air temperature signals in deep ST even after four months and the storage of precipitation signals in shallow ST after one month are widespread phenomena in China. Air temperature signals at 2m can propagate to the soil depths of 160 cm and 320 cm after 1 month and 2 months, respectively. The storage of antecedent air temperature and precipitation signals in ST is slightly weaker and stronger during April to September, respectively, which is related to more precipitation during growing season. The precipitation signals in ST rapidly weaken after 2 months. Moreover, the effects of accumulated precipitation and air temperature on the signal storage in ST have significant monthly variations and vary linearly with soil depth and latitude. The storage of antecedent air temperature or precipitation signals in ST exhibits an obvious decadal variation with a period of more than 50 years, and it may be resulted from the modulation of the global climate patterns which largely affect local air temperature and precipitation.

Constraints in Adapting the Climate Change in Konkan Region of Maharashtra

Changing climate is a serious environmental issue affecting agricultural production all overthe world. India is also facing the problem of increased mean temperature and irregularityof rainfall, and the Konkan region of Maharashtra is also not escaped from this issue. Thestudy was designed and conducted in the northern part of the Konkan region to understandthe constraints experienced by the farmers while adapting the climate change. The studywas carried in four villages of Palghar district with 245 respondents selected byproportionate random sampling method. Major constraints were expressed by the farmerswhile adapting the changing climate were, lack of credence on current weather forecastingsystem, poor accurate weather forecast information, irregular & low voltage capacity powersupply and seven others.

Impact of the Scandinavian pattern on long-lived cold surges over the South China Sea

This study investigates the influence of the Scandinavian (SCA) pattern on long-lived cold surges over the South China Sea (SCS). The results show that, different from the short-lived ones, the majority of long-lived cold surges over the SCS are preceded by a negative phase of quasi-stationary SCA pattern in the extratropics, which is characterized as a primary cyclonic center over the Scandinavian Peninsula and two anticyclonic ones over North Atlantic and central Siberia. This connection is mainly conducted through a continuous amplification of the high pressure anomalies over East Asia. On the other hand, the SCA-related anomalies also reveal identical responses as an increase in sea level pressure over East Asia and northerly flows over the SCS. Besides, the SCA pattern may influence the long-lived cold surges over the SCS by facilitating blocking occurrence through the extensive and quasi-stationary anticyclone over central Siberia. The present results have an implication for the extended weather forecast: The long-lasting circulation anomalies, such as the SCA pattern, can affect long-lasting weather phenomena in the regions which are located remotely in both the zonal and meridional directions, such as long-lived cold surges over the SCS.