Swell hindcast statistics for the Baltic Sea

The classic characterisation of swell as regular, almost monochromatic, wave trains does not necessarily accurately describe swell in water bodies shielded from the oceanic wave climate. In such enclosed areas the locally generated swell waves still contribute to processes at the air and seabed interfaces, and their presence can be quantified by partitioning wave components based on their speed relative to the wind. We present swell statistics for the semi-enclosed Baltic Sea using 20 years of swell-partitioned model data. The swell significant wave height was mostly under 2 m, and in the winter (DJF) the mean significant swell height was typically less than 0.4 m; higher swell was found in limited nearshore areas. Swell waves were typically short (under 5 s), with mean periods over 8 s being rare. In open-sea areas the average ratio of swell energy (to total energy) was mostly below 0.4 – significantly less than in the World Ocean. Certain coastal areas were swell dominated over half the time, mostly because of weak winds (U<5 m s−1) rather than high swell heights. Swell-dominated events with a swell height over 1 m typically lasted under 10 h. A cross-correlation analysis indicates that swell in the open sea is mostly generated from local wind sea when wind decays (dominant time lag roughly 15 h). Near the coast, however, the results suggest that the swell is partially detached from the local wind waves, although not necessarily from the weather system that generates them because the highest swell typically arrives with a roughly 10 h delay after the low-pressure system has already passed.

Comparison of total ozone retrieved from NOAA satellite and measured from Dobson spectrophotometer over Indian region

An attempt has been made to compare the total ozone retrieved from HIRS channel-9 of NOAA-12 satellite using ITPP software at the facility of lMDPS, New Delhi with that of conventional Dobson spectrophotometer over Indian network stations. The satellite-retrieved total ozone agrees within an accuracy of +-8% with that of Dobson-measured total ozone except during the passage of a weather system over the Indian region. It is seen that whenever a western disturbance is passing over north India and neighbourhood, the difference between the satellite-retrieved and Dobson-measured total ozone becomes more than +- 8% (or +-20 DU).

PREDICTION OF CLIMATE CHANGE USING ARIMA MODEL

The climate and weather system prediction has always attracted interest. Climate change risks including physical risks, liability risks and transition risks, it’s directly affecting the insurance industry. Climate change is majorly affecting the insurance sector; they are such as extreme heat during summer and extreme rainfall (Flood). It affects both insurance and reinsurance sector. Constructing the model is a necessary process but choosing the model which suits our data is very necessary. In those days the weather reports telecast in news but now even our smart phone notified the weather. In this paper study the climate prediction algorithms using R and also using Cost-free R language tool to forecast the climate using time ARIMA model for the Indian climate.

Precipitation simulation of synoptic scale systems over western Himalayan region using Advanced Regional Prediction System (ARPS) model

Operational weather prediction over western Himalayan region is a challenging job due to scarcity of data and complex topography that interacts with approaching weather system. Accurate prediction of complex weather phenomena requires dense data network which is difficult to establish in mountain due to complex terrain and hostile weather conditions over Himalaya. The alternate method to overcome this problem is by ingesting three-dimensional meteorological variables from global model’s analysis and forecast values as initial and lateral boundary conditions in meso-scale numerical models. Simultaneously, data assimilation is a potential tool in which non-conventional [satellite, radar and Automatic Weather Station (AWS)] and conventional (surface and upper air observations) data are ingested in the numerical models to generate high resolution and accurate initial fields for the initialization of the mesoscale model. In the present study, Advanced Regional Prediction System (ARPS) model has been used for the prediction of synoptic weather system known as Western Disturbance (WD) that affects the weather of western and central Himalaya during winter period (November – April).The ARPS model has been selected for this study because the model has its own objective analysis and quality control system. It has the capacity to ingest the satellite, Doppler weather radar data and other types of observations. Its assimilation system can also be used to overcome the problem of data scarcity in Himalayan region. In this study, initial and lateral boundary fields are taken from the T-80 spectral global model operationally used at National Centre for Medium Range Prediction (NCMRWF), Noida (UP), India. The global model’s analysis was taken as the initial condition and 24 hour’s interval forecasts as lateral boundary conditions. The model has been used for the simulation of few WDs for 96 hours (Four days). The comparison of ARPS simulation with T-80 forecast shows that the ARPS model could produce better results in respect of the circulation of WDs and hence it can be utilized for the operational weather prediction over the Indian region.

How intense daily precipitation depends on temperature and the occurrence of specific weather systems – an investigation with ERA5 reanalyses in the extratropical Northern Hemisphere

Precipitation and surface temperature are two of the most important variables that describe our weather and climate. Several previous studies investigated aspects of their relationship, for instance the climatological dependence of daily precipitation on daily mean temperature, P(T). However, the role of specific weather systems in shaping this relationship has not been analysed yet. This study therefore identifies the weather systems (WS) that are associated with intense precipitation days as a function of T, focusing on the question how this relationship, symbolically expressed as P(T,WS), varies regionally across the Northern Hemisphere and between seasons. To this end, we first quantify, if intense precipitation occurs on climatologically warmer or on colder days, respectively. In winter, over most continental and ocean regions, intense precipitation falls on warmer days apart from the Mediterranean area and regions in the lee of the Rocky Mountains, where intense precipitation is favoured on colder days. In summer, only at high latitudes intense precipitation is favoured on warmer days, whereas continental areas experience intense precipitation on colder days. For selected regions in Europe and North America, we then identify the weather systems that occur preferentially on days with intense precipitation (referred to as wet days). In winter, cyclones are slightly dominant on colder wet days, whereas warm conveyor belts and atmospheric rivers occur preferentially on warmer wet days. In summer, the overall influence of atmospheric rivers increases and the occurrence of weather systems depend less on wet day temperature. Wet days in the lee of the Rocky Mountains are influenced by most likely convective systems in anticyclones. Finally, we investigate P(T,WS) during the wettest and driest season in Central Europe and the Central US. In qualitative agreement with the results from the first part of this study, the wettest winter is warmer than normal in Central Europe but colder in the Central US, and the wettest summer is colder in both regions. The opposite holds for the driest winter and summer, respectively. During these anomalous seasons, both the frequency and the precipitation efficiency of weather systems changes in Central Europe, while the wettest and driest seasons in Central US mainly arise from a modified precipitation efficiency. Our results show that the precipitation-temperature-weather system relationship strongly depends on the region, and that (extreme) seasonal precipitation is influenced by the frequency and precipitation efficiency of the different weather systems. This regional variability is reflected in the relative importance of weather system frequency and efficiency anomalies for the formation of anomalously wet and dry seasons.

Sensitivity of Forecast Uncertainty to Different Microphysics Schemes within a Convection-Allowing Ensemble during SoWMEX-IOP8

A mesoscale convective system that occurred in southwestern Taiwan on 15 June 2008 is simulated using convection-allowing ensemble forecasts to investigate the forecast uncertainty associated with four microphysics schemes—the Goddard Cumulus Ensemble (GCE), Morrison (MOR), WRF single-moment 6-class (WSM6), and WRF double-moment 6-class (WDM6) schemes. First, the essential features of the convective structure, hydrometeor distribution, and microphysical tendencies for the different microphysics schemes are presented through deterministic forecasts. Second, ensemble forecasts with the same initial conditions are employed to estimate the forecast uncertainty produced by the different ensembles with the fixed microphysics scheme. GCE has the largest spread in most state variables due to its most efficient phase conversion between water species. By contrast, MOR results in the least spread. WSM6 and WDM6 have similar vertical spread structures due to their similar ice-phase formulae. However, WDM6 produces more ensemble spread than WSM6 does below the melting layer, resulting from its double-moment treatment of warm rain processes. The model simulations with the four microphysics schemes demonstrate upscale error growth through spectrum analysis of the root-mean difference total energy (RMDTE). The RMDTE results reveal that the GCE and WDM6 schemes are more sensitive to initial condition uncertainty, whereas the MOR and WSM6 schemes are relatively less sensitive to that for this event. Overall, the diabatic heating–cooling processes connect the convective-scale cloud microphysical processes to the large-scale dynamical and thermodynamical fields, and they significantly affect the forecast error signatures in the multiscale weather system.

A low-cost development of automatic weather station based on Arduino for monitoring precipitable water vapor

Water vapor is the engine of the weather system. Continuous monitoring of its variability on spatial and temporal scales is essential to help improve weather forecasts. This research aims to develop an automatic weather station at low cost using an Arduino microcontroller to monitor precipitable water vapor (PWV) on a micro-scale. The surface meteorological data measured from the BME280 sensor is used to determine the PWV. Our low-cost systems also consisted of a DS3231 real-time clock (RTC) module, a 16×2 liquid crystal display (LCD) module with an I²C, and a micro-secure digital (micro-SD) card. The core of the system employed the Arduino Uno surface mount device (SMD) R3 board. The measurement results for long-term monitoring at the tested sites (ITNY and GUWO) found that the daily mean error of temperature and humidity values were 1.30% and 3.16%, respectively. While the error of air pressure and PWV were 0.092% and 2.61%, respectively. The PWV value is higher when the sun is very active or during a thunderstorm. The developed weather system is also capable of measuring altitude on pressure measurements and automatically stores daily data. With a total cost below 50 dollars, all major and support systems developed are fully functional and stable for long-term measurements.

Statistical Analysis of Weather Parameters for Sustainable Flight Operation in Nigeria

The recent complications in the weather system, which oftentimes lead to flight cancellation, delay and diversion have become a critical issue in Nigeria. This study however considers the weather related parameters and their impacts on flight disruption in the country. Weather data (on thunderstorm, wind speed and direction, visibility and cloud cover) and flight data (delay, cancellation and diversion) were collected from Murtala International Airport, Ikeja-Lagos, Nigeria. The data covered the period between 2005 and 2020. However, Regional Climate Models (RCMs) were also used to run climate data projections between year 2020 and 2035 in the study region. The study employed Statistical Package for Social Sciences (SPSS) software for the descriptive and inferential analysis. Time series analysis, Pearson Moment Correlation for interrelationship among the weather parameters and the flight disruption data, and multiple linear regression analysis were applied to determine the influence of weather parameters on flight disruption data. Results show that cloud cover and high visibility are negatively correlated. Wind speed has positive relationship with wind direction; and an inverse relationship between visibility, thunderstorm, and fog. Direct relationship exists between highest visibility and thick dust, wind speed and cloud cover. Thick dust, wind speed and cloud cover indicate increased visibility level in the study area. Flight delay is prominent over flight diversion and cancellation, which indicates their relevance in air traffic of the study area. The prediction model indicates high degree of cloud cover at the beginning of every year and later declines sharply in 2035, the visibility flattens out by the year 2025, and low pattern of thick dust was calculated in the same pattern in 2011, 2016 and 2027. Based on this conclusion, the study recommends accurate weather reporting and strict compliance to safety regulations, and attention should be paid to changing pattern of weather parameters in order to minimize fight related disasters.

LONG RANGE IOT COMMUNICATION

The article deals with the design of wireless automation system. The proposed systems compare wireless data transmission devices. The main components and their parameters, which are necessary for building such system and base steps how to create and test a device are described. The created systems can serve as a suitable basis for remote monitoring and control systems in open space applications such as weather system or small-scale home automation system and can be used as an example in the education of students in fields such as Automation or Mechatronics.