Modeling the Daily Average Temperature Data Using Stochastic Process and Neural Networks for Weather Derivatives

월별 기후통계량의 조화해석에 의해 생성한 일 기온 자료가 생물계절모형의 입력자료로서 적합한지 여부를 평가하여 농림업 부문 기후시나리오 응용정보 제작 상오류를 제거하기 위해 본 연구를 수행하였다. 서울관측소의 1971-2000 평년 월별 일 최고기온과 최저기온 평균값으로부터 조화해석에 의해 365일 간 기온자료를 생성하였다. 이것을 널리 검증된 온도시간 기반의 벚꽃 개화모형에 입력하여 휴면, 발아, 개화 등 주요 식물계절을 추정하였다. 같은 기간 중 실측기온자료에 의해 모형을 구동시켜 얻은 결과와 비교한 바, 연차변이를 전혀 반영하지 못하는 것은 물론, 휴면해제 25일 단축, 강제 휴면기간 57일 연장, 발아 14일 지연, 개화 13일 지연등 평균값도 크게 달라 식물계절을 크게 왜곡시키는 것으로 판단되었다. 대안으로서 확률추정기법에 의해 일기상자료를 생성하고 이를 이용하여 모형을 구동한 결과 실측결과에 비해 휴면해제 6일 단축, 강제휴면기간 10일 단축, 발아 3일 지연, 개화 2일 지연 등으로 조화해석자료 사용에 비해 크게 개선되었음을 확인하였다. 연차변이양상 역시 실측기온에 의한 모의결과와 크게 다르지 않아, 향후 이 자료를 농업부문 전자기후도 제작에 적용하면 기후변화 적응정책 수립을 실용수준에서 지원할 수 있을 것으로 보인다. Daily temperature data produced by harmonic analysis of monthly climate summary have been used as an input to plant phenology model. This study was carried out to evaluate the performance of the harmonic based daily temperature data in prediction of major phenological developments and to apply the results in improving decision support for agricultural production in relation to the climate change scenarios. Daily maximum and minimum temperature data for a climatological normal year (Jan. 1 to Dec. 31, 1971-2000) were produced by harmonic analysis of the monthly climate means for Seoul weather station. The data were used as inputs to a thermal time - based phenology model to predict dormancy, budburst, and flowering of Japanese cherry in Seoul. Daily temperature measurements at Seoul station from 1971 to 2000 were used to run the same model and the results were compared with the harmonic data case. Leaving no information on annual variation aside, the harmonic based simulation showed 25 days earlier release from endodormancy, 57 days longer period for maximum cold tolerance, delayed budburst and flowering by 14 and 13 days, respectively, compared with the simulation based on the observed data. As an alternative to the harmonic data, 30 years daily temperature data were generated by a stochastic process (SIMMETEO + WGEN) using climatic summary of Seoul station for 1971-2000. When these data were used to simulate major phenology of Japanese cherry for 30 years, deviations from the results using observed data were much less than the harmonic data case: 6 days earlier dormancy release, 10 days reduction in maximum cold tolerance period, only 3 and 2 days delay in budburst and flowering, respectively. Inter-annual variation in phenological developments was also in accordance with the observed data. If stochastically generated temperature data could be used in agroclimatic mapping and zoning, more reliable and practical aids will be available to climate change adaptation policy or decision makers.

Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US

Originally published in 2005, Weather Derivative Valuation covers all the meteorological, statistical, financial and mathematical issues that arise in the pricing and risk management of weather derivatives. There are chapters on meteorological data and data cleaning, the modelling and pricing of single weather derivatives, the modelling and valuation of portfolios, the use of weather and seasonal forecasts in the pricing of weather derivatives, arbitrage pricing for weather derivatives, risk management, and the modelling of temperature, wind and precipitation. Specific issues covered in detail include the analysis of uncertainty in weather derivative pricing, time-series modelling of daily temperatures, the creation and use of probabilistic meteorological forecasts and the derivation of the weather derivative version of the Black-Scholes equation of mathematical finance. Written by consultants who work within the weather derivative industry, this book is packed with practical information and theoretical insight into the world of weather derivative pricing.

Production of greenhouse lettuce requires high energy input and high capital cost. Modelling optimal growth and yield was used to determine factors to improve production efficiency. Year-round light levels are very variable in northern latitude areas. In winter, when natural daylight is short and light levels are low, supplementary lighting can be used. In summer, when high light intensity prevails, shading can be used. The market requires a certain minimal fresh weight per living lettuce plant, which is sold with its root. Artificial neural networks (ANN) to model the final fresh weight were investigated. Daily light, temperature, and relative humidity data were obtained with a greenhouse environment control computer. A video camera was used to capture a daily or hourly image of the plants to determine percentage canopy cover. Only weekly data were used in this study. Plant (fresh) weight at harvest at a specific age (days after transplanting to final space) was modelled by using these weekly data. As the crop grew, the most recent weekly data were added to predict final fresh weight. Eight lighting experiments were conducted over 18 months and generated 64 data points (cases) for modelling. Using ANN, the final weight can be predicted either by weekly average light, weekly percent canopy cover, or both, with similar levels of error. The predicted plant weight was close to observed values, with an average difference of about 10 to 11 %, although the root mean square (RMS) errors were high. The RMS errors can be reduced by grouping inputs of light so that these 64 cases can be increased to 380 cases. The resulting ANN models permit simulation by end-users to understand the interrelationship between final plant weight, crop age, and daily light level. Preliminary results indicated that the growth models can be used as a guide to avoid unnecessary supplementary lighting.

A stochastic model driven by generalized hyperbolic Levy process with seasonal mean and volatility for the daily average temperature data is used. This model is perfect to capture the semi-substantial tails and skewness observed in our data. The model is fitted to 11 years of daily data recorded in 16 cities of Ethiopia. We obtained the market risk factor from the historical data of temperature by calculating the CDD and HDD, which is a new addition to the concept of weather derivatives and main contribution of this paper. We get a promising result which enable us to obtain more accurate value for price of temperature derivative. Moreover, the price of weather derivative written on temperature indices is calculated, using Monte Carlo simulation technique. Numerical examples are presented to indicate the accuracy of the method. Our results show that the model performs well to predict CDD and HDD indices.

The objective of this study was to improve the simulation of node number in soybean cultivars with determinate stem habits. A nonlinear model considering two approaches to input daily air temperature data (daily mean temperature and daily minimum/maximum air temperatures) was used. The node number on the main stem data of ten soybean cultivars was collected in a three-year field experiment (from 2004/2005 to 2006/2007) at Santa Maria, RS, Brazil. Node number was simulated using the Soydev model, which has a nonlinear temperature response function [f(T)]. The f(T) was calculated using two methods: using daily mean air temperature calculated as the arithmetic average among daily minimum and maximum air temperatures (Soydev tmean); and calculating an f(T) using minimum air temperature and other using maximum air temperature and then averaging the two f(T)s (Soydev tmm). Root mean square error (RMSE) and deviations (simulated minus observed) were used as statistics to evaluate the performance of the two versions of Soydev. Simulations of node number in soybean were better with the Soydev tmm version, with a 0.5 to 1.4 node RMSE. Node number can be simulated for several soybean cultivars using only one set of model coefficients, with a 0.8 to 2.4 node RMSE.

Heading and flowering are two key phenological stages in the growth process of winter wheat. It is of great significance for agricultural management and scientific research to accurately monitor and forecast the heading and flowering dates of winter wheat. However, the monitoring accuracy of existing methods based on remote sensing needs to be improved, and these methods cannot realize forecasting in advance. This study proposed an accumulated temperature method (ATM) for monitoring and forecasting the heading and flowering dates of winter wheat from the perspective of thermal requirements for crop growth. The ATM method consists of three key procedures: (1) extracting the green-up date of winter wheat as the starting point of temperature accumulation with the dynamic threshold method from remotely sensed vegetation index (VI) time-series data, (2) calculating the accumulated temperature and determining the thermal requirements from the green-up date to the heading date or the flowering date based on phenology observation samples, and (3) combining the satellite-derived green-up date, daily temperature data, and thermal requirements to monitor and forecast the heading date and flowering date of winter wheat. When applying the ATM method to winter wheat in the North China Plain during 2017–2019, the root mean square error (RMSE) for the estimated heading date was between 4.76 and 6.13 d and the RMSE for the estimated flowering date was between 5.30 and 6.41 d. By contrast, the RMSE for the heading and flowering dates estimated by the widely used maximum vegetation index method was approximately 10 d. Furthermore, the forecasting accuracy of the ATM method was also high, and the RMSE was approximately 6 d. In summary, the proposed ATM method can be used to accurately monitor and forecast the heading and flowering dates of winter wheat in large spatial scales and it performs better than the existing maximum vegetation index method.

Dew point temperature (Td) is a precise measure of atmospheric moisture. A significant number of models for studying crop–climate interactions and earth processes require daily Td as an input. However, limited availability of Td data is a major barrier for applications of these models. In this paper, we present a daily Td estimation method for the northern Great Plains (NGP). The daily Td estimation method presented here requires daily maximum, minimum, and mean temperature data. Data from six sites in the NGP were used for the study. These sites record hourly Td data from relative humidity. Length of the time series is 14 yr (1986–1999). Four different regression-based approaches were adopted and applied to all sites. Eventually, the best method was adopted based on its performance. The model evaluation statistics show that the selected model performs satisfactorily for these six sites. For example, root mean square error (RMSE), mean absolute error (MAE), and d index (ranges between 0 and 1, where 1 indicates no model error) values for North Platte, NE, application are 3.23, 2.55, and 0.97, respectively. The selected method was further applied to five additional locations in the NGP, and again it performed satisfactorily. For example, RMSE, MAE, and d index values for McCook, NE, application are 2.6, 2.0, and 0.98, respectively. From the model evaluation, we conclude that the model performed satisfactorily and will be quite useful in estimating Td.

Dew point temperature (Td) is a precise measure of atmospheric moisture. A significant number of models for studying crop–climate interactions and earth processes require daily Td as an input. However, limited availability of Td data is a major barrier for applications of these models. In this paper, we present a daily Td estimation method for the northern Great Plains (NGP). The daily Td estimation method presented here requires daily maximum, minimum, and mean temperature data. Data from six sites in the NGP were used for the study. These sites record hourly Td data from relative humidity. Length of the time series is 14 yr (1986–1999). Four different regression‐based approaches were adopted and applied to all sites. Eventually, the best method was adopted based on its performance. The model evaluation statistics show that the selected model performs satisfactorily for these six sites. For example, root mean square error (RMSE), mean absolute error (MAE), and d index (ranges between 0 and 1, where 1 indicates no model error) values for North Platte, NE, application are 3.23, 2.55, and 0.97, respectively. The selected method was further applied to five additional locations in the NGP, and again it performed satisfactorily. For example, RMSE, MAE, and d index values for McCook, NE, application are 2.6, 2.0, and 0.98, respectively. From the model evaluation, we conclude that the model performed satisfactorily and will be quite useful in estimating Td.

The study investigates accuracy of a new modeling scheme, subset adaptive neuro fuzzy inference system (subset ANFIS), in estimating the daily reference evapotranspiration (ET0). Daily weather data of relative humidity, solar radiation, air temperature, and wind speed from three stations in Central Anatolian Region of Turkey were utilized as input to the applied models. The input data set for modeling the ET0 was divided to several subsets to calibrate the local data using a local modeling-based ANFIS. The estimates obtained from subset ANFIS models were compared with those of the M5 model tree (M5Tree), ANFIS models and ANN. Mean absolute error (MAE), root mean square error (RMSE), and model efficiency factor criteria were applied for analysis of models. The accuracy of M5Tree (from 15.3% to 32.5% in RMSE, from 14.4% to 24.2% in MAE), ANN (from 24.3% to 65.3% in RMSE, from 34.1% to 47% in MAE) and ANFIS (from 17.4% to 35.4% in RMSE, from 10.8% to 28.3% in MAE) models was significantly increased using subset ANFIS for estimating da ily ET0.

This study aims to analyse temperature trend, variation, and change point patterns in Bayelsa State, Nigeria over a 31-year period (1992-2022). The daily temperature data used for the study were obtained from the Nigerian Meteorological Agency (NIMET). The daily maximum and minimum temperature data were further processed to obtain the annual maximum, minimum, and mean temperatures. Several statistical tests were utilized to investigate the temperature trend and to detect change point year. Linear regression and Mann-Kendall tests were employed to establish if there are significant trend in the temperature data series. Distribution-free CUSUM test and Sequential Mann-Kendall test were used to identify the change point year. The results reveal a statistically significant increasing trend in annual mean temperature at a rate of 0.020°C per year (0.2°C per decade or 2.0°C per century). Annual maximum temperature showed a marginally significant positive trend of 0.037°C per year, while annual minimum temperature exhibited a non-significant negative trend of -0.043°C per year. Change point analysis identified significant shifts in annual mean temperature patterns around 2007 and 2015. The findings indicate that Bayelsa State is experiencing warming consistently with global climate change patterns. However, cooler temperature is still experienced during the early hours and evening session as not significant increasing trend was observed in the annual minimum temperature.

Estimation of degree-days for different climatic zones of North-East India

Daily mean temperature is the important atmospheric parameter; also have been one of the essential data sets for climate change study. And by now the studies on daily mean temperature mainly focus on Kriging theory based methods, which is to realize gridding, interpolating, extrapolating and analysis in time dimension and/or space dimension. However, it's supposed that small scale variation part in the spatial data has been met stationary assumption in Kriging based methods. It's may not be suitable for sorts of atmospheric data, and daily mean temperature included. In this paper, we propose a new method for modeling daily mean temperature and prove it by the daily mean temperature data set downloaded from China Meteorological Data Sharing Service System, which lasts 52a(1961-2012)including over 800 meteorological stations nationwide distributed in China. Considering the spatial correlation among this spatial data and longitude\latitude\height, to build a new multiplicative multiple regression model based correlation coefficients to describe the big scale expectation function in daily mean temperature data set and meanwhile in comparison with traditional Kriging method. The result shows that the zero-mean residual from this new method, which represents the small scale random function in this data set, doesn't show spatial correlation with longitude\latitude\height and its spatial heterogeneity(0°/45°/90°/135°direction semivariograms) performs in nearly isotropy with better stationarity. Meanwhile the standard deviation and stable standard deviation almost smaller than those from traditional Kriging method, 95.63% and 91.80% respectively. In conclusion, the new method models daily mean temperature data set better than traditional Kriging method. After cross validation, the mean error (ME) is −0.003 and root-mean-square error (RMSE) is 1.652 from the new method, and traditional Kriging method results are 0.005 and 1.877 respectively.

Background:Estimation of tropospheric radio refractivity is significant in the planning and design of terrestrial communication links.Methods:In this study, the monthly average daily atmospheric pressure, relative humidity and temperature data obtained from the National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 - June 2005) for Osogbo (Latitude 7.470N, Longitude 4.290E, and 302.0 m above sea level) were used to estimate the monthly tropospheric radio refractivity. The monthly average daily global solar radiation with other meteorological parameters was used to developed one, two, three and four variable correlation(s) tropospheric radio refractivity models for the location. The accuracy of the proposed models are validated using statistical indicator of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash - Sutcliffe Equation (NSE) and Index of Agreement (IA).Results:In each case one empirical model was recommended based on their exceptional performances after ranking, except for the two variation correlations with two empirical models. The recommended models were further subjected to ranking from which the three variable correlations model that relates the radio refractivity with the absolute temperature, relative humidity and global solar radiation was found more suitable for estimating tropospheric radio refractivity for Osogbo with R2= 100.0%, MBE = -0.2913 N-units, RMSE = 0.3869 N-units, MPE = 0.0811%, NSE = 99.9999% and IA = 100.00%.Conclusion:The newly developed recommended models (Equations 16c, 17d, 17f, 18d and 19) can be used for estimating daily and monthly values of tropospheric radio refractivity with higher accuracy and has good compliance to highly varying climatic conditions for Osogbo and regions of similar climatic information.

Background:Estimation of tropospheric radio refractivity is significant in the planning and design of terrestrial communication links.Methods:In this study, the monthly average daily atmospheric pressure, relative humidity and temperature data obtained from the National Aeronautics and Space Administration (NASA) during the period of twenty two years (July 1983 - June 2005) for Osogbo (Latitude 7.470N, Longitude 4.290E, and 302.0 m above sea level) were used to estimate the monthly tropospheric radio refractivity. The monthly average daily global solar radiation with other meteorological parameters was used to developed one, two, three and four variable correlation(s) tropospheric radio refractivity models for the location. The accuracy of the proposed models are validated using statistical indicator of coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), Nash - Sutcliffe Equation (NSE) and Index of Agreement (IA).Results:In each case one empirical model was recommended based on their exceptional performances after ranking, except for the two variation correlations with two empirical models. The recommended models were further subjected to ranking from which the three variable correlations model that relates the radio refractivity with the absolute temperature, relative humidity and global solar radiation was found more suitable for estimating tropospheric radio refractivity for Osogbo with R2= 100.0%, MBE = -0.2913 N-units, RMSE = 0.3869 N-units, MPE = 0.0811%, NSE = 99.9999% and IA = 100.00%.Conclusion:The newly developed recommended models (Equations 16c, 17d, 17f, 18d and 19) can be used for estimating daily and monthly values of tropospheric radio refractivity with higher accuracy and has good compliance to highly varying climatic conditions for Osogbo and regions of similar climatic information.