scholarly journals Modelling Agro-Met Station Observations Using Genetic Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-8
Author(s):  
Prashant Kumar ◽  
Bimal K. Bhattacharya ◽  
C. M. Kishtawal ◽  
Sujit Basu
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Relative Humidity ◽  
Data Fitting ◽  
Prediction Methods ◽  
Training Algorithm ◽  
Near Surface ◽  
The Past ◽  
Forecast Models ◽  
Weather Parameters

The present work discusses the development of a nonlinear data-fitting technique based on genetic algorithm (GA) for the prediction of routine weather parameters using observations from Agro-Met Stations (AMS). The algorithm produces the equations that best describe the temporal evolutions of daily minimum and maximum near-surface (at 2.5-meter height) air temperature and relative humidity and daily averaged wind speed (at 10-meter height) at selected AMS locations. These enable the forecasts of these weather parameters, which could have possible use in crop forecast models. The forecast equations developed in the present study use only the past observations of the above-mentioned parameters. This approach, unlike other prediction methods, provides explicit analytical forecast equation for each parameter. The predictions up to 3 days in advance have been validated using independent datasets, unknown to the training algorithm, with impressive results. The power of the algorithm has also been demonstrated by its superiority over persistence forecast used as a benchmark.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

scholarly journals Using ERA-Interim Reanalysis output for creating datasets of energy-relevant climate variables

2017 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim Reanalysis is presented. A number of different bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting those calculated from ERA-Interim to those based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed and relative humidity, available at either 3 or 6 h timescales over the period 1979-2014. This dataset is available to anyone through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S), and can be accessed at present from (ftp://ecem.climate.copernicus.eu). The benefit of performing bias-adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against observations.

scholarly journals Forecast of the trend of heavy fog based on the VARIMAX model

2021 ◽  
Vol 257 ◽  
pp. 03013
Author(s):  
Boyang Peng ◽  
Yuchi Meng ◽  
Dapai Shi ◽  
Mingyu Dai ◽  
Hao Zhou ◽  
...  
Keyword(s):  
Wind Speed ◽  
Meteorological Factors ◽  
Air Pressure ◽  
Dew Point ◽  
Observation Data ◽  
Near Surface ◽  
Temperature Index ◽  
The Past ◽  
Visibility Index ◽  
Incorrect Data

This paper works out relationship between visibility and near-surface meteorological factors. The formation of heavy fog is affected by meteorological factors near the ground and fog in the past period. In this paper, we abstract and simplify the problem as a time series problem. First, the airport AWOS observation data is reprocessed, and some missing and incorrect data are supplemented and corrected. Then draw a distribution map of “Visibility-Near-surface Meteorological Factors” to intuitively grasp the correlation between them. Finally, model the classic VARIMAX to fit the mapping relationship between visibility and near-surface meteorological factors. The results show temperature has the greatest impact on visibility index, positively correlated with it; secondly, dew point temperature index negatively correlated with it. The results show that, with the temperature low and the humidity high, the water vapor in the atmosphere is more likely to condense into mist, which is not easy to dissipate, resulting in reduced visibility. The indicators related to air pressure and wind speed are positively correlated with visibility, indicating that the increase in air pressure and the increase in wind speed will promote the dissipation of heavy fog. Generally speaking, the MOR index fits better with near-surface meteorological factors.

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

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 173-180
Author(s):  
NAVNEET KAUR ◽  
M.J. SINGH ◽  
SUKHJEET KAUR
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Linear Regression Method ◽  
Post Monsoon ◽  
Sunshine Hours ◽  
Annual Minimum Temperature ◽  
Weather Parameters

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

scholarly journals Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

2017 ◽  
Vol 9 (2) ◽  
pp. 471-495 ◽  
Author(s):  
Philip D. Jones ◽  
Colin Harpham ◽  
Alberto Troccoli ◽  
Benoit Gschwind ◽  
Thierry Ranchin ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Climate Variables ◽  
Climate Data ◽  
Near Surface ◽  
Bias Adjustment ◽  
Dewpoint Temperature ◽  
Data Store

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

scholarly journals A bias corrected WRF mesoscale fire weather dataset for Victoria, Australia 1972-2012

2016 ◽  
Vol 66 (3) ◽  
pp. 281
Author(s):  
Timothy Brown ◽  
Graham Mills ◽  
Sarah Harris ◽  
Domagoj Podnar ◽  
Hauss Reinbold ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Fire Danger ◽  
Management Decision ◽  
Fire Weather ◽  
Near Surface ◽  
Australian Water ◽  
Hourly Temperature ◽  
Extensive Evaluation ◽  
Correction Technique

Climatology data of fire weather across the landscape can provide science-based evidence for informing strategic decisions to ameliorate the impacts (at times extreme) of bushfires on community socio-economic wellbeing and to sustain ecosystem health and functions. A long-term climatology requires spatial and temporal data that are consistent to represent the landscape in sufficient detail to be useful for fire weather studies and management purposes. To address this inhomogeneity problem for analyses of a variety of fire weather interests and to provide a dataset for management decision-support, a homogeneous 41-year (1972-2012), hourly interval, 4 km gridded climate dataset for Victoria has been generated using a combination of mesoscale modelling, global reanalysis data, surface observations, and historic observed rainfall analyses. Hourly near-surface forecast fields were combined with Drought Factor (DF) fields calculated from the Australian Water Availability Project (AWAP) rainfall analyses to generate fields of hourly fire danger indices for each hour of the 41-year period. A quantile mapping (QM) bias correction technique utilizing available observations during 1996-2012 was used to ameliorate any model biases in wind speed, temperature and relative humidity. Extensive evaluation was undertaken including both quantitative and case study qualitative assessments. The final dataset includes 4-km surface hourly temperature, relative humidity, wind speed, wind direction, Forest Fire Danger Index (FFDI), and daily DF and Keetch-Byram Drought Index (KBDI), and a 32-level full three-dimensional volume atmosphere.

scholarly journals A Reliability Assessment of the NCEP/FNL Reanalysis Data in Depicting Key Meteorological Factors on Clean Days and Polluted Days in Beijing

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 481
Author(s):  
Chao Liu ◽  
Jianping Guo ◽  
Bihui Zhang ◽  
Hengde Zhang ◽  
Panbo Guan ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Factors ◽  
Surface Wind ◽  
Lower Boundary ◽  
Temperature Inversion ◽  
Surface Wind Speed ◽  
Reanalysis Data ◽  
Near Surface ◽  
The Difference

In this study, based on the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data, the reliability and performances of their application on clean days and polluted days (based on the PM2.5 mass concentrations) in Beijing were assessed. Conventional meteorological factors and diagnostic physical quantities from the NCEP/FNL data were compared with the L-band radar observations in Beijing in the autumns and winters of 2017–2019. The results indicate that the prediction reliability of the temperature was the best compared with those of the relative humidity and wind speed. It is worth noting that the relative humidity was lower and the near-surface wind speed was higher on polluted days from the NCEP/FNL data than from the observations. As far as diagnostic physical quantity is concerned, it was revealed that the temperature inversion intensity depicted by the NCEP/FNL data was significantly lower than that from the observations, especially on polluted days. For example, the difference in the temperature inversion intensity between the NCEP/FNL data and the observation ranged from −0.56 to −0.77 °C on polluted days. In addition, the difference in the wind shears between the NCEP/FNL reanalysis data and the observations increased to 0.40 m/s in the lower boundary layer on polluted days compared with that on clean days. Therefore, it is suggested that the underestimation of the relative humidity and temperature inversion intensity, and the overestimation of the near-surface wind speed should be seriously considered in simulating the air quality in the model, particularly on polluted days, which should be focused on more in future model developments.

scholarly journals Influence of Weather Parameters and Thermal Time Approach on Green Gram at Coimbatore, Tamil Nadu

2020 ◽  
pp. 1-5
Author(s):  
S. A. Naveen ◽  
S. Kokilavani ◽  
S. P. Ramanathan ◽  
G. A. Dheebakaran ◽  
S. Anitta Fanish
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Tamil Nadu ◽  
Green Gram ◽  
Research Centre ◽  
Maximum Temperature ◽  
Sowing Dates ◽  
Sunshine Hours ◽  
Diurnal Range ◽  
Weather Parameters

An investigation was carried out at the Agro Climate Research Centre, Tamil Nadu Agricultural University, on the effect of weather parameters on the green gram yield sown at various sowing dates during the rabi season of 2019. At various sowing dates, two green gram cultivars, VBN 4 and ADT 3, were sown. For both cultivars, the phonological crop length decreased with delays in sowing dates beyond October 23rd. The yield of green gram sown on 23rd October was significantly higher than the crops sown on 30th October and 6th November. The weather parameters Maximum Temperature (Tmax), Diurnal Range (Trange), Bright Sunshine Hours (BSS), Relative Humidity (RH I), Wind Speed (WS) were found to be negatively correlated with seed yield whereas Minimum Temperature (Tmin), Relative Humidity (RH II), Vapour Pressure (VP) were found to be positively correlated with the yield of green gram. The accurate prediction of green gram yield could be done with the maximum temperature, bright sunshine hours, wind speed and with thermal indices especially hygrothermal unit II with 82 percent, accuracy level.

scholarly journals Potato late blight disease prediction using meteorological parameters in Northern Himalayas of India

2021 ◽  
Vol 23 (3) ◽  
pp. 310-315
Author(s):  
W. A. DAR ◽  
F. A. PARRY ◽  
B. A. BHAT
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Late Blight ◽  
Weather Conditions ◽  
Potato Late Blight ◽  
Disease Development ◽  
Potato Blight ◽  
Late Blight Disease ◽  
Weather Parameters ◽  
Blight Disease

Weather parameters play an important role in the spread of potato late blight of caused by Phytophthora infestans (Mont.) de Bary has historically been serious disease of potatoes through worldwide, including India. Due to spatial variation in prevailing weather conditions, its severity varies from region to region. Disease development process and the weather parameters are well understood and have been utilized for disease developing forecasting models and decision support system. Therefore, an experiment was conducted for two consecutive cropping seasons (2017 & 2018) to develop a forecasting model against late blight of potato using stepwise regression analysis for Northern Himalayas in India. Maximum and minimum temperature, relative humidity, rainfall and wind speed appeared to be most significant factors in the potato late blight disease development. The meteorological conditions conducive for the development of potato late blight disease were characterized. Maximum and minimum temperatures in the range of 15.0 – 28.0°C and 2.0 – 12.0°C were found favorable for potato blight disease. Similarly, relative humidity, rainfall and wind speed in the range of 85 - 95 per cent, 15.5 - 20.75 mm and 1.0 - 5.5 Km h-1, respectively, were conducive for potato late blight disease which are helpful in disease development.

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