This document investigates thunderstorm occurrence in Tawau, Malaysia during the southern monsoon season from 2011-2013. It analyzes data on cloud cover, wind speed and direction, temperature, humidity, and thunderstorm frequency. Results show thunderstorms are most frequent during the winter monsoon months of December-February when winds are class 3 (4-7 knots). During the summer monsoon, thunderstorms are less frequent from late May to August, indicating dry conditions. Based on the wind and weather correlations, the document suggests space activities would be best scheduled during the middle of June to July or middle of August.
The document discusses a study of the vertical structure of the atmosphere using radiosonde data from Chennai, India. It analyzes temperature, relative humidity, and dew point profiles during rainy and non-rainy periods. On rainy days, the temperature and dew point were close together indicating a very humid and saturated atmosphere allowing precipitation. Non-rainy days showed lower humidity and wider separation of temperature and dew point profiles. Skew-T log-P diagrams further illustrated the stable and unstable atmospheric conditions associated with rainy and non-rainy periods. The data helps understand severe weather events like heavy rain and predict atmospheric conditions.
This document discusses the procedures and tools used in weather forecasting. It describes how weather data is collected from over 9,500 observation stations and 7,400 ships worldwide and transmitted to analysis centers. Forecasts are made using synoptic charts, computer modeling, and satellite imagery from geosynchronous and low-Earth orbiting satellites. Forecasts can be short-range up to 48 hours, medium-range from 3 days to 3 weeks, or long-range from 2 weeks to a season. The goal of weather forecasting is to continue advancing techniques to better predict high-impact weather events.
This document summarizes a study that characterized cyclones in the Bay of Bengal using cyclone tracking data from the International Best Track Archive for Climate Stewardship (IBTrACS) from 1986 to 2016. The following key points are made:
- Most cyclones occurred during October and November and had landfalls along the northwest coast of the Bay of Bengal, affecting India.
- There is an inverse relationship between wind speed and pressure - high wind speeds are associated with low pressures, resulting in cyclones.
- Spatial analyses showed maximum wind speeds and lowest pressures predominantly in the northeast region of the Bay of Bengal.
- There is a decreasing trend observed in the number of cyclones occurring in the Bay
1) The document examines seasonal forecasts for global wind energy during the summer, focusing on regions where wind resource is abundant and highly variable.
2) It analyzes wind resource availability and variability from 1981-2011 to identify key regions of interest, including Patagonia/Chile, Central Sahara/Sahel/Kenya, Central-Western India, Central-Southern Western Continent/Western China, and Northern Australia/Tasmania.
3) It assesses the skill of seasonal wind forecasts from 1981-2010 against observations, finding the highest skill in regions like Northeast Coast/Eastern Brasil/Northwest Coast, Southeast Continent/India, and Sahel/Western Angola/Western Namib
This is the paper for our final project in our Numerical Weather Prediction class. For this project, we analyzed model output from a Nested Regional Climate Model (NRCM), which is an adaptation of the Advanced Research WRF (ARW). The model output variables analyzed were outgoing long wave radiation (OLR) and precipitation (convective plus non-convective). The goal of this research project was to determine why errors were occurring in the model, and what could be done to correct them. In this paper, we provide some insight into why these errors occurred, particularly errors within the model which equaled or surpassed the overall mean climate error.
This document summarizes research on downburst occurrence in Brazil. It discusses regions in Brazil where downbursts have been reported, including the Amazon Basin, South, Southeast and Northeast regions. It also analyzes convective environments favorable for downbursts, noting that high CAPE and low CIN values indicate favorable conditions. Specific meteorological thresholds are discussed that could indicate downburst occurrence, such as a decrease in equivalent potential temperature, increase in surface pressure, and wind gusts over 10m/s. The document reviews literature on downburst prediction and characteristics.
Weather forecasting has evolved significantly over thousands of years from early observational methods to modern computer-based modeling. Early forecasting relied on patterns observed over generations, while today satellites and sensors collect global data that feeds complex models. Forecasting accuracy has improved markedly, though challenges remain as forecasts range further in time due to the chaotic nature of the atmosphere. Continued advances in computing power, data collection, and scientific understanding aim to further refine predictions.
The document provides information about weather maps and weather concepts. It discusses key elements of weather maps including isobars, pressure cells, wind direction and speed. It explains that high pressure cells bring clear skies while low pressure cells bring cloud and rain. It also summarizes different types of rainfall including convectional, orographic and frontal rainfall. Seasons are determined by the positioning of pressure systems with lows over northern Australia in summer and highs in winter.
The document discusses a study of the vertical structure of the atmosphere using radiosonde data from Chennai, India. It analyzes temperature, relative humidity, and dew point profiles during rainy and non-rainy periods. On rainy days, the temperature and dew point were close together indicating a very humid and saturated atmosphere allowing precipitation. Non-rainy days showed lower humidity and wider separation of temperature and dew point profiles. Skew-T log-P diagrams further illustrated the stable and unstable atmospheric conditions associated with rainy and non-rainy periods. The data helps understand severe weather events like heavy rain and predict atmospheric conditions.
This document discusses the procedures and tools used in weather forecasting. It describes how weather data is collected from over 9,500 observation stations and 7,400 ships worldwide and transmitted to analysis centers. Forecasts are made using synoptic charts, computer modeling, and satellite imagery from geosynchronous and low-Earth orbiting satellites. Forecasts can be short-range up to 48 hours, medium-range from 3 days to 3 weeks, or long-range from 2 weeks to a season. The goal of weather forecasting is to continue advancing techniques to better predict high-impact weather events.
This document summarizes a study that characterized cyclones in the Bay of Bengal using cyclone tracking data from the International Best Track Archive for Climate Stewardship (IBTrACS) from 1986 to 2016. The following key points are made:
- Most cyclones occurred during October and November and had landfalls along the northwest coast of the Bay of Bengal, affecting India.
- There is an inverse relationship between wind speed and pressure - high wind speeds are associated with low pressures, resulting in cyclones.
- Spatial analyses showed maximum wind speeds and lowest pressures predominantly in the northeast region of the Bay of Bengal.
- There is a decreasing trend observed in the number of cyclones occurring in the Bay
1) The document examines seasonal forecasts for global wind energy during the summer, focusing on regions where wind resource is abundant and highly variable.
2) It analyzes wind resource availability and variability from 1981-2011 to identify key regions of interest, including Patagonia/Chile, Central Sahara/Sahel/Kenya, Central-Western India, Central-Southern Western Continent/Western China, and Northern Australia/Tasmania.
3) It assesses the skill of seasonal wind forecasts from 1981-2010 against observations, finding the highest skill in regions like Northeast Coast/Eastern Brasil/Northwest Coast, Southeast Continent/India, and Sahel/Western Angola/Western Namib
This is the paper for our final project in our Numerical Weather Prediction class. For this project, we analyzed model output from a Nested Regional Climate Model (NRCM), which is an adaptation of the Advanced Research WRF (ARW). The model output variables analyzed were outgoing long wave radiation (OLR) and precipitation (convective plus non-convective). The goal of this research project was to determine why errors were occurring in the model, and what could be done to correct them. In this paper, we provide some insight into why these errors occurred, particularly errors within the model which equaled or surpassed the overall mean climate error.
This document summarizes research on downburst occurrence in Brazil. It discusses regions in Brazil where downbursts have been reported, including the Amazon Basin, South, Southeast and Northeast regions. It also analyzes convective environments favorable for downbursts, noting that high CAPE and low CIN values indicate favorable conditions. Specific meteorological thresholds are discussed that could indicate downburst occurrence, such as a decrease in equivalent potential temperature, increase in surface pressure, and wind gusts over 10m/s. The document reviews literature on downburst prediction and characteristics.
Weather forecasting has evolved significantly over thousands of years from early observational methods to modern computer-based modeling. Early forecasting relied on patterns observed over generations, while today satellites and sensors collect global data that feeds complex models. Forecasting accuracy has improved markedly, though challenges remain as forecasts range further in time due to the chaotic nature of the atmosphere. Continued advances in computing power, data collection, and scientific understanding aim to further refine predictions.
The document provides information about weather maps and weather concepts. It discusses key elements of weather maps including isobars, pressure cells, wind direction and speed. It explains that high pressure cells bring clear skies while low pressure cells bring cloud and rain. It also summarizes different types of rainfall including convectional, orographic and frontal rainfall. Seasons are determined by the positioning of pressure systems with lows over northern Australia in summer and highs in winter.
Meteorologists are able to forecast weather by studying weather maps containing data on variables like temperature, wind, and pressure collected from surface measurements and atmospheric measurements using radiosondes. Weather maps show fronts, pressure centers, and trends that can be used to predict weather patterns and precipitation. Forecasters analyze changes between maps over multiple days to anticipate future conditions based on these patterns.
1) The document provides seasonal forecasts for autumn solar photovoltaic (PV) energy potential in key regions globally based on solar irradiance data from 1981-2011.
2) It identifies regions where solar irradiance is most abundant and variable, and where seasonal forecast skill is highest one month in advance, such as Spain, East Australia, and Indonesia.
3) An example operational forecast for autumn 2011 predicts areas likely to have above, below, or normal solar irradiance that season.
1) The document analyzes seasonal forecasts for global solar PV energy availability in autumn.
2) It identifies several key regions where solar GHI is both abundant and highly variable in autumn, including Spain/Portugal, Indonesia, eastern Australia, and Tanzania/Kenya Coast.
3) It assesses the skill of climate forecast models in predicting autumn solar GHI variability and magnitude up to 1 month in advance, finding the highest skill in regions like Spain/Portugal, Indonesia, northeast USA/Caribbean, and northeast Australia.
Weather forecasting is important for agriculture as it allows farmers to plan for and adjust to upcoming weather conditions. There are different types of forecasts including nowcasting (up to 1 day), short range (1-3 days), medium range (3-10 days), and long range (>10 days). Accurate forecasts can help minimize losses from adverse weather and optimize input use through timely adjustments. Forecasting methods include synoptic analysis of surface and upper air charts, statistical analysis of historical weather data, and numerical weather prediction using physics-based models. The reliability of forecasts depends on factors like weather data collection and dissemination systems, forecaster experience, and forecasting technology.
An Investigation of Weather Forecasting using Machine Learning TechniquesDr. Amarjeet Singh
1. The document discusses using machine learning techniques to forecast weather based on recorded data from weather stations.
2. It investigates using supervised learning algorithms like regression to predict future weather conditions based on historical data.
3. The models can provide short-term weather forecasts more quickly and using fewer resources than traditional complex physics models run on high-performance computing systems.
5 - K Prasad - Weather forecasting in modern age-Sep-16indiawrm
Numerical weather prediction models have greatly improved forecasting accuracy through increased computing power, enhanced observational data from satellites and other sources, and more advanced modeling techniques. Global models run by organizations like NOAA provide short, medium, and extended forecasts of weather parameters on grids as fine as 20-25km. Regional models run at finer resolutions. Observational data is collected through a global network and transmitted via telecommunication systems to processing centers, which generate forecast products available in real-time.
The document discusses various types of weather forecasting for agriculture including nowcasting, short range forecasting, medium range forecasting, and long range forecasting. It describes the different time periods each covers and how they can help farmers plan agricultural operations. Weather forecasting is important for agriculture because weather greatly impacts crop yields, and accurate forecasts can help minimize losses from adverse conditions.
Weather prediction technology is a global, big data enterprise. This talk will describe the huge quantities of information that make modern weather prediction possible, from satellite and radar data to surface observations and the output from numerical weather prediction models. The role of smartphones and other mobile devices for distributing forecasts and weather information will be discussed and the future of weather prediction will be outlined.
Meteorology is the scientific study of the atmosphere and weather forecasting. Significant advances occurred in the 18th century with observing networks, and in the 20th century with the development of computers enabling improved weather forecasting. Meteorology studies temperature, pressure, water vapor and how they interact and change over time at various spatial scales from local to global. It is related to other atmospheric sciences and has applications across many fields. Common weather instruments measure rainfall, temperature, pressure, wind speed and direction, sunshine, and humidity.
The document discusses weather forecasting. It describes the process of forecasting as involving observation, collection and transmission of weather data, plotting and analysis of this data, and then analysis of weather maps and other tools to formulate a forecast. Key tools used in forecasting include weather maps, satellite and radar images, and numerical weather prediction models. The accuracy of forecasts depends on knowledge of weather conditions over a wide area.
This technical report presents preliminary analysis results from two GNSS stations near the epicenter of large earthquakes in Thessaly, Greece on March 3rd and 4th, 2021. The earthquakes were magnitudes 6.0 and 5.9. Analysis of data from stations KLOK and LARM found permanent displacements of -4.0cm east and -2.7cm north at KLOK and 0.4cm east and 0cm vertical at LARM due to the earthquakes. More detailed analysis is still underway to better estimate the offsets.
This document defines key weather vocabulary words including weather, atmosphere, temperature, front, wind, anemometer, meteorologist, and weather map. It provides short definitions for each term - weather is defined as the happenings in the atmosphere at a certain time, atmosphere as the air that surrounds Earth, and so on. A meteorologist is described as a scientist who studies weather and the atmosphere.
This document provides information about reading weather maps and forecasting the weather. It defines key terms like air masses, cold fronts, warm fronts, and pressure systems. It also describes an activity where students work in pairs to research current temperatures and forecasts for different cities using Weather Underground, then plot the temperatures on a map. Finally, it provides homework questions about identifying fronts, pressure systems, using data to forecast fronts, and how wind maps relate to weather patterns.
The document describes the basics of creating a European Monsoon Time Scale, which is a chronological scale used to study the past, present, and future movements and impacts of the European Monsoon. It involves recording weather events on a 365-day scale over many years to identify patterns. Examples from an Indian Monsoon Time Scale show clusters of low pressure systems and rainfall trends that correlated to the monsoon's movement over time. The document advocates creating a similar scale for the European Monsoon to better understand its relationship to weather problems and natural disasters in the region.
The document discusses various aspects of weather forecasting by the National Weather Service and U.S. Navy. It describes the different types of forecasts produced, including area forecasts by major Navy units, flight forecasts for successive flight stages, and local forecasts by ships and stations. It also outlines the roles of organizations like the National Oceanic and Atmospheric Administration, National Weather Service, and Navy Meteorology and Oceanography Command in coordinating weather data collection and forecasting activities.
Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the
Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five twoyear
periods show a marked seasonal dependence. The surface temperature near the south pole over this time
decreased by 2 K from 91.7±0.3 to 89.7±0.5 K while at the north pole the temperature increased by 1 K from
90.7±0.5 to 91.5±0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the subsolar
latitude. As the latitude changed, the maximum temperature remained constant at 93.65±0.15 K. In 2010
our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the
mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were
lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist
ground brought on by seasonal methane precipitation and evaporation.
This document summarizes a study of vegetation fire data from 2009-2014 in Greater Manchester, England. It finds that vegetation fire density was highest near urban areas and along river valleys. The driest springs in 2010, 2011, and 2013 saw the most fires. While weather influences fire occurrence, the timing of holiday periods also impacted it. The study defines wildfires using two approaches and finds patterns vary significantly depending on the definition used. Risk is influenced by land cover type and the interface between rural and urban areas.
Impact of Climate Modes such as El Nino on Australian RainfallAlexander Pui
This document analyzes the impact of large-scale climate modes like ENSO, IOD, and SAM on daily and subdaily rainfall characteristics in east Australia. It finds that the occurrence of rainfall events, rather than average rainfall intensity, is most influenced by these climate modes. This is shown to be associated with changes in the time between wet spells. Furthermore, ENSO remains the leading driver of rainfall variability in east Australia, especially further inland during winter and spring. The results have implications for water resource management and how climate models capture rainfall variability.
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTAIAEME Publication
Understanding of scintillation is a significant occurrence in the design of communication satellite system. In this research, two years (January 2015-December 2016) tropospheric scintillation records dig out from Astra 2E/2F/2G at 28.2 oE Satellite path link observation at (Lat: 6.7 oN, Long: 3.23 oE) at Ota, southwest Nigeria, at 12.245 GHz and an elevation angle 59.9o. The result and analysis were likened with some reliable tropospheric scintillation estimate models in order to acquire best model for Ota environment. The result findings revealed that the Karasawa model provides the minimum percentage error for scintillation fades and enhancements of approximately 0.57 % at 0.1 unavailability of time and 6.93 % at 0.01 unavailability of time respectively. Hence, Karasawa model is the most found suitable for the estimation of transmission loss in this region. Also, scintillation intensity is noticed to be high throughout the non-rainy season likened to rainy season months. Conversely, the model must be verified more by means of higher frequency band like Ka and V bands to affirm the accurateness of the model. The statistics provided in this work will assistance in fade margin for performance and antenna sizing required for communication satellite link.
This document discusses generating a revised Typical Meteorological Year (TMY) solar radiation data for Armidale, Australia that considers cloudy days. It begins by explaining what TMY data is and how it is typically generated without considering cloudy days. It then defines clear and cloudy days based on cloud cover measurements. The methodology section describes using the Finkelstein-Schafer statistical method to generate the original TMY from 23 years of solar radiation data, without accounting for cloudy days. The document aims to generate a revised TMY that considers cloudy days and analyze the impacts on the expected solar radiation potential.
Meteorologists are able to forecast weather by studying weather maps containing data on variables like temperature, wind, and pressure collected from surface measurements and atmospheric measurements using radiosondes. Weather maps show fronts, pressure centers, and trends that can be used to predict weather patterns and precipitation. Forecasters analyze changes between maps over multiple days to anticipate future conditions based on these patterns.
1) The document provides seasonal forecasts for autumn solar photovoltaic (PV) energy potential in key regions globally based on solar irradiance data from 1981-2011.
2) It identifies regions where solar irradiance is most abundant and variable, and where seasonal forecast skill is highest one month in advance, such as Spain, East Australia, and Indonesia.
3) An example operational forecast for autumn 2011 predicts areas likely to have above, below, or normal solar irradiance that season.
1) The document analyzes seasonal forecasts for global solar PV energy availability in autumn.
2) It identifies several key regions where solar GHI is both abundant and highly variable in autumn, including Spain/Portugal, Indonesia, eastern Australia, and Tanzania/Kenya Coast.
3) It assesses the skill of climate forecast models in predicting autumn solar GHI variability and magnitude up to 1 month in advance, finding the highest skill in regions like Spain/Portugal, Indonesia, northeast USA/Caribbean, and northeast Australia.
Weather forecasting is important for agriculture as it allows farmers to plan for and adjust to upcoming weather conditions. There are different types of forecasts including nowcasting (up to 1 day), short range (1-3 days), medium range (3-10 days), and long range (>10 days). Accurate forecasts can help minimize losses from adverse weather and optimize input use through timely adjustments. Forecasting methods include synoptic analysis of surface and upper air charts, statistical analysis of historical weather data, and numerical weather prediction using physics-based models. The reliability of forecasts depends on factors like weather data collection and dissemination systems, forecaster experience, and forecasting technology.
An Investigation of Weather Forecasting using Machine Learning TechniquesDr. Amarjeet Singh
1. The document discusses using machine learning techniques to forecast weather based on recorded data from weather stations.
2. It investigates using supervised learning algorithms like regression to predict future weather conditions based on historical data.
3. The models can provide short-term weather forecasts more quickly and using fewer resources than traditional complex physics models run on high-performance computing systems.
5 - K Prasad - Weather forecasting in modern age-Sep-16indiawrm
Numerical weather prediction models have greatly improved forecasting accuracy through increased computing power, enhanced observational data from satellites and other sources, and more advanced modeling techniques. Global models run by organizations like NOAA provide short, medium, and extended forecasts of weather parameters on grids as fine as 20-25km. Regional models run at finer resolutions. Observational data is collected through a global network and transmitted via telecommunication systems to processing centers, which generate forecast products available in real-time.
The document discusses various types of weather forecasting for agriculture including nowcasting, short range forecasting, medium range forecasting, and long range forecasting. It describes the different time periods each covers and how they can help farmers plan agricultural operations. Weather forecasting is important for agriculture because weather greatly impacts crop yields, and accurate forecasts can help minimize losses from adverse conditions.
Weather prediction technology is a global, big data enterprise. This talk will describe the huge quantities of information that make modern weather prediction possible, from satellite and radar data to surface observations and the output from numerical weather prediction models. The role of smartphones and other mobile devices for distributing forecasts and weather information will be discussed and the future of weather prediction will be outlined.
Meteorology is the scientific study of the atmosphere and weather forecasting. Significant advances occurred in the 18th century with observing networks, and in the 20th century with the development of computers enabling improved weather forecasting. Meteorology studies temperature, pressure, water vapor and how they interact and change over time at various spatial scales from local to global. It is related to other atmospheric sciences and has applications across many fields. Common weather instruments measure rainfall, temperature, pressure, wind speed and direction, sunshine, and humidity.
The document discusses weather forecasting. It describes the process of forecasting as involving observation, collection and transmission of weather data, plotting and analysis of this data, and then analysis of weather maps and other tools to formulate a forecast. Key tools used in forecasting include weather maps, satellite and radar images, and numerical weather prediction models. The accuracy of forecasts depends on knowledge of weather conditions over a wide area.
This technical report presents preliminary analysis results from two GNSS stations near the epicenter of large earthquakes in Thessaly, Greece on March 3rd and 4th, 2021. The earthquakes were magnitudes 6.0 and 5.9. Analysis of data from stations KLOK and LARM found permanent displacements of -4.0cm east and -2.7cm north at KLOK and 0.4cm east and 0cm vertical at LARM due to the earthquakes. More detailed analysis is still underway to better estimate the offsets.
This document defines key weather vocabulary words including weather, atmosphere, temperature, front, wind, anemometer, meteorologist, and weather map. It provides short definitions for each term - weather is defined as the happenings in the atmosphere at a certain time, atmosphere as the air that surrounds Earth, and so on. A meteorologist is described as a scientist who studies weather and the atmosphere.
This document provides information about reading weather maps and forecasting the weather. It defines key terms like air masses, cold fronts, warm fronts, and pressure systems. It also describes an activity where students work in pairs to research current temperatures and forecasts for different cities using Weather Underground, then plot the temperatures on a map. Finally, it provides homework questions about identifying fronts, pressure systems, using data to forecast fronts, and how wind maps relate to weather patterns.
The document describes the basics of creating a European Monsoon Time Scale, which is a chronological scale used to study the past, present, and future movements and impacts of the European Monsoon. It involves recording weather events on a 365-day scale over many years to identify patterns. Examples from an Indian Monsoon Time Scale show clusters of low pressure systems and rainfall trends that correlated to the monsoon's movement over time. The document advocates creating a similar scale for the European Monsoon to better understand its relationship to weather problems and natural disasters in the region.
The document discusses various aspects of weather forecasting by the National Weather Service and U.S. Navy. It describes the different types of forecasts produced, including area forecasts by major Navy units, flight forecasts for successive flight stages, and local forecasts by ships and stations. It also outlines the roles of organizations like the National Oceanic and Atmospheric Administration, National Weather Service, and Navy Meteorology and Oceanography Command in coordinating weather data collection and forecasting activities.
Meridional brightness temperatures were measured on the surface of Titan during the 2004–2014 portion of the
Cassini mission by the Composite Infrared Spectrometer. Temperatures mapped from pole to pole during five twoyear
periods show a marked seasonal dependence. The surface temperature near the south pole over this time
decreased by 2 K from 91.7±0.3 to 89.7±0.5 K while at the north pole the temperature increased by 1 K from
90.7±0.5 to 91.5±0.2 K. The latitude of maximum temperature moved from 19 S to 16 N, tracking the subsolar
latitude. As the latitude changed, the maximum temperature remained constant at 93.65±0.15 K. In 2010
our temperatures repeated the north–south symmetry seen by Voyager one Titan year earlier in 1980. Early in the
mission, temperatures at all latitudes had agreed with GCM predictions, but by 2014 temperatures in the north were
lower than modeled by 1 K. The temperature rise in the north may be delayed by cooling of sea surfaces and moist
ground brought on by seasonal methane precipitation and evaporation.
This document summarizes a study of vegetation fire data from 2009-2014 in Greater Manchester, England. It finds that vegetation fire density was highest near urban areas and along river valleys. The driest springs in 2010, 2011, and 2013 saw the most fires. While weather influences fire occurrence, the timing of holiday periods also impacted it. The study defines wildfires using two approaches and finds patterns vary significantly depending on the definition used. Risk is influenced by land cover type and the interface between rural and urban areas.
Impact of Climate Modes such as El Nino on Australian RainfallAlexander Pui
This document analyzes the impact of large-scale climate modes like ENSO, IOD, and SAM on daily and subdaily rainfall characteristics in east Australia. It finds that the occurrence of rainfall events, rather than average rainfall intensity, is most influenced by these climate modes. This is shown to be associated with changes in the time between wet spells. Furthermore, ENSO remains the leading driver of rainfall variability in east Australia, especially further inland during winter and spring. The results have implications for water resource management and how climate models capture rainfall variability.
INVESTIGATION AND EVALUATION OF SCINTILLATION PREDICTION MODELS AT OTAIAEME Publication
Understanding of scintillation is a significant occurrence in the design of communication satellite system. In this research, two years (January 2015-December 2016) tropospheric scintillation records dig out from Astra 2E/2F/2G at 28.2 oE Satellite path link observation at (Lat: 6.7 oN, Long: 3.23 oE) at Ota, southwest Nigeria, at 12.245 GHz and an elevation angle 59.9o. The result and analysis were likened with some reliable tropospheric scintillation estimate models in order to acquire best model for Ota environment. The result findings revealed that the Karasawa model provides the minimum percentage error for scintillation fades and enhancements of approximately 0.57 % at 0.1 unavailability of time and 6.93 % at 0.01 unavailability of time respectively. Hence, Karasawa model is the most found suitable for the estimation of transmission loss in this region. Also, scintillation intensity is noticed to be high throughout the non-rainy season likened to rainy season months. Conversely, the model must be verified more by means of higher frequency band like Ka and V bands to affirm the accurateness of the model. The statistics provided in this work will assistance in fade margin for performance and antenna sizing required for communication satellite link.
This document discusses generating a revised Typical Meteorological Year (TMY) solar radiation data for Armidale, Australia that considers cloudy days. It begins by explaining what TMY data is and how it is typically generated without considering cloudy days. It then defines clear and cloudy days based on cloud cover measurements. The methodology section describes using the Finkelstein-Schafer statistical method to generate the original TMY from 23 years of solar radiation data, without accounting for cloudy days. The document aims to generate a revised TMY that considers cloudy days and analyze the impacts on the expected solar radiation potential.
1) The document analyzes seasonal forecasts for global wind energy availability in autumn.
2) It identifies several key regions where wind resource is both abundant and highly variable between years, making them most suitable for seasonal wind forecasting.
3) The forecasts are evaluated against past data and found to have the highest skill in predicting wind resource variability, magnitude, and uncertainty in certain regions like Patagonia, parts of Africa, Asia, and Australia.
This document discusses generating optimistic, normal, and pessimistic estimates of global solar radiation in Armidale, New South Wales, Australia based on 23 years of daily radiation data. Typical meteorological year (TMY) data was previously generated for Armidale using the Finkelstein-Schafer statistical method to select the most representative month from each year. This study aims to provide upper and lower limits around the normal TMY values using the same method by selecting months with the highest and lowest radiation levels.
This study analyzed the correlation between aerosol optical depth and total column water vapor near the coast of Africa using satellite data from OMI and reanalysis data from ECMWF. The results showed:
1) In October 2009 and 2010, there was a consistent positive correlation between the two factors.
2) In January 2009, the correlation was weak and inconsistent, with most data points clustered on one side.
3) During a dust storm in March 2010, there was an indirect negative correlation between aerosols and water vapor.
The results refute the "semi-direct effect" hypothesis that absorbing aerosols warm clouds and increase humidity. Further research is needed to better understand the relationship between aeros
Air Pollution Climatology Of Bhopal And Gwalior ECRD IN
This document analyzes air pollution climatology data from Bhopal and Gwalior, India over a 5-year period. It finds that April has the highest mixing height and ventilation, suggesting best pollutant dispersion. January and October are worst for vertical dispersion. Daytime generally allows for better dispersion than nighttime. Bhopal experiences more unstable conditions and better dispersion than Gwalior. Industries should be located south of cities to minimize pollutant effects, as prevailing winds are from the north.
Identification of three_dominant_rainfall_regions_Lasriama Siahaan
This document describes a study that identifies three dominant rainfall regions in Indonesia based on an analysis of rainfall data from 1961-1993. The study uses a "double correlation method" to objectively classify regions based on the similarity of their annual rainfall cycles. The results identify three distinct regions: Region A covers southern Indonesia, Region B covers northwest Indonesia, and Region C covers parts of Sulawesi and Maluku. Each region exhibits a unique annual rainfall cycle and response to factors like the El Niño-Southern Oscillation. The relationships between sea surface temperatures and rainfall variability in each region are also analyzed.
This document discusses seasonal forecasts for global wind energy in winter. It begins by showing maps of average winter wind resource and variability based on reanalysis data. Several regions with abundant and variable wind resources are identified. The document then assesses the skill of climate forecast models to predict winter wind variability up to 1 month in advance. Maps show where forecasts best match observations. Key regions with both high wind potential and skilled forecasts are identified. Finally, an example operational probabilistic forecast for winter 2011 wind resource is presented, focused on the most skillful regions.
Parametric analysis of ground temperature profile in bwari north central nigeriaAlexander Decker
This study analyzed ground temperature data collected over five years in Bwari, Nigeria to predict future climate changes and natural disasters. The results showed that net earth radiation has increased and rainfall has not been able to offset it. May typically had the highest average monthly ground temperatures. 2012 experienced major flooding in Nigeria. While 2013 may see increased solar radiation, reduced flooding is predicted; heavier flooding may occur in 2014 or 2015 due to the continued rise in net earth radiation not being balanced by natural forces.
This document analyzes heavy rainfall occurrences in northeast India using daily rainfall data from 15 stations over 31 years. It finds:
1) The most favorable locations for heavy rainfall events are between latitudes 27.5°N and 28.1°N.
2) The most common time period for these events is June 10 to August 5. July sees the largest number of events, followed by June and August.
3) There is a significant decreasing trend in the aggregate number of extreme rainfall events across the region between 1973-2001. Convective available potential energy is decreasing while convective inhibition energy is increasing, consistent with the decreasing trend in heavy rainfall events.
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
diurnal temperature range trend over North Carolina and the associated mechan...Sayem Zaman, Ph.D, PE.
This study seeks to investigate the variability and presence of trends in the diurnal surface air temperature range
(DTR) over North Carolina (NC) for the period 1950–2009. The significance trend test and the magnitude of trends were determined using the non-parametric Mann–Kendall test and the Theil–Sen approach, respectively.
Statewide significant trends (p b 0.05) of decreasing DTR were found in all seasons and annually during the analysis period. The highest (lowest) temporal DTR trends of magnitude −0.19 (−0.031) °C/decade were found in summer (winter). Potential mechanisms for the presence/absence of trends in DTR have been highlighted. Historical
data sets of the three main moisture components (precipitation, total cloud cover (TCC), and soil moisture) and
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Similar to An Investigation of Several Thunderstorms During Southern Monsoon Over Tawau Area (20)
An Investigation of Several Thunderstorms During Southern Monsoon Over Tawau Area
1. An Investigation of Several Thunderstorms During
Southern Monsoon Over Tawau Area
Wayan Suparrta*¹, M. N. Syamim Idris², Wahyu S.Putro³
Space Science Centre(ANGKASA),Institute of Climate Change, Universiti Kebangsaan Malaysia,Bangi,
Selangor,Malaysia
Abstract-This paper presents to investigate the
occurrence of thunderstorm in Tawau area during
Southern Monsoon for finding the suitable location of
the space launcher in future. An attempt was made by
analyze and compare the cloud cover (Octas) data,
wind speed and direction data, PTH data, RSPWV
data and thunderstorm frequency data taken in the
year of 2011, 2012 and 2013. It shows that events
occur during winter monsoon, the frequency of
thunderstorm occur is high especially in the month of
December, January and February. While during the
summer monsoon, can be seen a dry season occur
which no frequent of thunderstorm occur especially
in the month of late May 2011 to August 2013.Thus,
based on this result, suggestion can be made if any of
space activities should be occur during this period.
Keywords—Convective System, Tawau area,
Artificial Neural Network (ANN), Surface
Meteorological, Rainfall, and precipitation
I. Introduction
1.1 BACKGROUND
The convective system is like a complex of
thunderstorm or electrical storm with hydrostatic
approximation, geotropic and wind gradient [1]. The
development early warning systems it can help the
human to forecasting weather especially
thunderstorm activity. A lot of parameters and
variables use to develop model forecasting and
estimation. In the line with this development, we
focused on East Malaysia (Borneo) region especially
Tawau area. Tawau is one of state in the Sabah
Malaysia has a severe Convective System activity
during the summer monsoon in June, July and August
(JJA) months [2].
It know that the summer monsoon with a clear day is
crucial to estimate the variation of rainfall and
precipitation to develop an early warning system for
the space activity programme. For this purpose, we
constructed the estimation rainfall and precipitation
using surface meteorology data. The model of
estimation is an alternative to seeing the variation of
rainfall and precipitation measurement over
convective system activity when the thunderstorm
data is absent. In the current study of convective
system activity, Suparta et al. [3] was found the
activity of Mesoscale Convective System (MCS) has
increased during December, January and February
(DJF) months over Tawau area. Pielke [4] studied the
mesoscale convergence as the environment's
precondition for thunderstorm development. His
study demonstrates link between surface moisture
and heat fluxes and cumulus convective rainfall.
2. Methodology
A. Dataset and Location
The location of this study was Tawau Station (96481)
area with coordinate of Latitude 4° 19' N / longitude
118° 07' E, with Mean Sea Level (M.S.L) of 17.5
m.To investigate the thunderstorm occurrence over
Tawau area, some parameters need to be finding and
recorded .The parameters are wind speed(knots) and
direction data, cloud cover (octas) data, thunderstorm
data, pressure(Pa), temperature(°Celsius) and
humidity (mm) data, and Radiosonde Precipitable
Water Vapor,RSPWV(mm) data. The data was taken
in three different years which are from 2011 to 2013.
The cloud cover (Octas) data, thunderstorm
occurrence data, wind speed and direction data was
taken from Meteorological Malaysia .The pressure,
temperature and humidity(PTH) data was taken from
surface meteorological weather underground website
with duration per hour .The RSPWV data was taken
from the Wyoming University also with duration per
hour.
B. Data Analyzing
The surface meteorological data pressure,
temperature and humidity (PTH) ,thunderstorm and
cloud cover (Octas) data taken were plotted and
RSPWV data also were plotted in three different
years in the same plotted figure.Then, the data was
compared and correlated with thunderstorm to be
analyzed and estimate the thunderstorm occur
specifically over Tawau area.
3. Results and Discussion
Figure 1 shows that the trending and variation of the
wind speed distribution over Tawau area in the year
of 2011, 2012 and 2013. From the data, each year
experienced of wind speed class three (4-7knots)
from south direction which can be harm to the space
launcher to depart into the south direction.
2. During winter monsoon, (November to March) in
north direction the wind speed is in class two (1-
4knots) frequently happening and sometimes in class
three (4-7knots). However, in south direction every
month had class three speeds. Consider the wind
speed class three as can harm the space launcher if
want to takeoff during the month of winter monsoon
whether in north or south direction.
While during summer monsoon (late May to
September), the wind speed is in class two in the north
but not frequently as during winter monsoon. But, in
south direction the wind speed had class three and
frequently happened. Hence, it is suitable for the
space launcher to takeoff during summer monsoon in
north direction because of the wind speed in north is
not high as much as during winter monsoon and south
direction in summer monsoon.
In Figure 2 shows the cloud cover (octas) over Tawau
area .It was nearly correspond to the wind speed and
direction. During the Southeast monsoon, in August
the cloud cover is in between 5 to 7 octas which
indicates that there is event occur with 5 octas cloud
cover as the wind speed during that month is
frequently low (class 1 and 2) in north and high (class
3) in south direction. In contrast, during the
Northwest monsoon can be seen in the month of
December where the cloud cover is in between 6 and
7 octas hourly and also the wind speed is in class two
frequently same as in the month of March. However,
in the month of January and February there are some
event occurs where the cloud cover is 8 octas(fully
covered).
This is same as the statement of C. J. G. Morris [10]
where the cloud cover increase as the wind speed
increases
Figure 3 shows that the thunderstorm frequency over
Tawau area. During the southeast monsoon as a case
study, in year 2012 and 2013 the thunderstorm in June
and July had clearly same of 11 thunderstorm and 8
thunderstorm occurrence in the month respectively.
In July, it indicates among the lowest frequent of
thunderstorm occur in a month compare to others
hence possibly had a dry season in that month.
The thunderstorm data can be related with the cloud
octas data. During Southeast Monsoon in August of
2011 and 2012 when the octas is decreased, the
thunderstorm frequency is increased. This is not same
as the statement of C. Panneerselvam [11] in India
where the same events occur relating the
thunderstorm occurrence and the octas value stated
that there is an increased of thunderstorm occur with
the increase of the octas value.
In order to identify the thunderstorm occurs as in
figure 3, the pressure(P),Temperature(T) and
Humidity(PTH)of the air were plotted as in figure 4.
During Southeast monsoon in July of 2011,2012 and
2013, the temperature is increase as the pressure and
humidity are decreased and hence this correlated with
the thunderstorm in July where it has 8 thunderstorm
occur fewer than any other month.
In contrast, in the early of August 2013, the
temperature had decreased rapidly (very minimum
compared to others temperature value in 2011,2012
and 2013) ,contribute to the very high humidity and
also increased in presurre in the air.However, after
that events in 2013 the temperature had increased but
fluctuated effected the humidity and pressure in vice
versa .Hence related with the thunderstorm
data,indicates there is thunderstorm occur when the
first decrease of temperature,and increase humidity
and pressure before the thunderstorm not frequently
occur in the middle and late of August 2013 explained
only seven thunderstorm occur during that month.
In figure 5, the RSPWV data shows correlated with
the thunderstorm events during Southeast monsoon.
It is found that there is an increased of RSPWV during
late of May to early of June similarly there is
thunderstorm occur frequently during that time in
each year. It reached the maximum of RSPWV
recorded in early of June 2011 where it can be seen
that time in figure 4 PTH Data also there is
temperature decreased, pressure and humidity
increased contribute to the occurrence of
thunderstorm.
However, the RSPWV has minimum value in the
early of August 2013 where it decreased to had almost
zero value .During that time, there is slightly
decreased of temperature value but consistently
increase of humidity and pressure indicates that there
is a day in that month that had possibility of
thunderstorm occur. According to this, there is not
necessarily that thunderstorm will not occur if there
is decreased of RSPWV.There is a theory that there is
possibility there is only thunderstorm in the evening
on that day but not in the morning .This can be found
from Suparta[13] statement that the lightning will
occur when the PWV was increased in the evening by
at least 5 mm.
3. A. Wind Speed and Wind Direction Data over Tawau area
Years Wind Direction Wind Speed (Frequency class distribution)
2011
2012
2013
Figure 1 : Wind Rose variation over Tawau area in the year of 2011, 2012 and 2013.
B. Cloud Cover(Octas) Data.
4. Figure 2 : Cloud Cover (Octas) Data in 2011, 2012 and 2013
C. Thunderstorm Data
Figure 3 : Thunderstorm frequency data over Tawau area in year 2011, 2012 and 2013
D. Thunderstorm in association with PTH Data
5. Figure 4 : The PTH Data taken by weather underground website in year 2011, 2012 and 2013.
E. Thunderstorm in association with Radiosonde PWV (RSPWV)Data.
Figure 5 : RSPWV Data taken from Wyoming University in year 2011, 2012 and 2013.
6. 5. Conclusion
The correlations between the thunderstorm and PTH,
RSPWV, cloud cover (Octas) and wind speed and
direction can be simply concluded. Based on the
result, we can say that:
I. The condition space launcher over Tawau
area during Southeast Monsoon event is must
be wind speed to class two(1-4knots), and
the space launcher depart from south area to
the north as the wind speed is frequently high
in south direction .
The correlation between the parameters
shown in the results also consistently related
.This can be summarized as the space
launcher departs must be in certain events.
The events correlated are with high
temperature, low pressure and humidity
(PTH), gives the lower in Radiosonde
Precipitable Water Vapor (RSPWV) and
hence contributed to no thunderstorm occur.
II. This parameter mentioned earlier contributed
to some events and characteristics of Tawau
area during Southeast Monsoon which is
averagely dry or summer day.
Hence, the suggestions suitable for the space
launcher to depart during Southeast
Monsoon are during middle of June to July,
and also middle of August(JJA).
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