scholarly journals Climate Data for Field Trials: Onsite Micro Stations Versus ClimateNA

2021 ◽  
Author(s):  
Zhengyang Ye ◽  
Gregory O’Neill ◽  
Tongli Wang
Keyword(s):  
Prediction Error ◽  
Field Trials ◽  
Weather Data ◽  
Response Functions ◽  
Random Errors ◽  
Climate Data ◽  
Average Prediction Error ◽  
Provenance Trials ◽  
Weather Stations ◽  
Alternative Sources

Abstract Background Studies in diverse environmental fields require accurate climate data for point locations that are often distant from reliable public weather stations. ‘Onsite’ micro weather stations can be established directly at research locations, but purchase, establishment, and maintenance costs and data gaps can limit their feasibility. Alternatively, climate data for point locations can be predicted from ClimateNA, a publicly available software package, but the prediction accuracy in remote and mountainous locations is uncertain. Results We compared ClimateNA predictions with observations from onsite weather stations located at 11 interior spruce provenance trials in British Columbia, Canada. We found that ClimateNA predictions were highly accurate for temperature variables (average prediction error 0.77°C; most R2 values > 0.99) but moderate for precipitation variables (average prediction error 27mm; 0.21 < R2 values < 0.58) when compared with onsite weather data (with random errors identified). Growth response functions developed with the two data sources showed similar patterns for temperature variables. Conclusions Our results suggest that 1) temperature variables can be accurately predicted at remote and mountainous locations using ClimateNA; 2) precipitation variables are more accurately predicted with ClimateNA than with onsite weather stations, which were considerably affected by random factors; and 3) response functions provide an effective, independent tool to assess alternative sources of climate data. Our results recommend the use of ClimateNA over onsite weather stations, except where highly accurate precipitation data are required, in which case, high-quality onsite weather stations must be established and carefully maintained.

scholarly journals Evaluation of NASA POWER Reanalysis Products to Estimate Daily Weather Variables in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1207
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Wind Speed ◽  
Bias Correction ◽  
Goodness Of Fit ◽  
Mediterranean Climate ◽  
Weather Data ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Weather Variables ◽  
Weather Stations

This study aims to evaluate NASA POWER reanalysis products for daily surface maximum (Tmax) and minimum (Tmin) temperatures, solar radiation (Rs), relative humidity (RH) and wind speed (Ws) when compared with observed data from 14 distributed weather stations across Alentejo Region, Southern Portugal, with a hot summer Mediterranean climate. Results showed that there is good agreement between NASA POWER reanalysis and observed data for all parameters, except for wind speed, with coefficient of determination (R2) higher than 0.82, with normalized root mean square error (NRMSE) varying, from 8 to 20%, and a normalized mean bias error (NMBE) ranging from –9 to 26%, for those variables. Based on these results, and in order to improve the accuracy of the NASA POWER dataset, two bias corrections were performed to all weather variables: one for the Alentejo Region as a whole; another, for each location individually. Results improved significantly, especially when a local bias correction is performed, with Tmax and Tmin presenting an improvement of the mean NRMSE of 6.6 °C (from 8.0 °C) and 16.1 °C (from 20.5 °C), respectively, while a mean NMBE decreased from 10.65 to 0.2%. Rs results also show a very high goodness of fit with a mean NRMSE of 11.2% and mean NMBE equal to 0.1%. Additionally, bias corrected RH data performed acceptably with an NRMSE lower than 12.1% and an NMBE below 2.1%. However, even when a bias correction is performed, Ws lacks the performance showed by the remaining weather variables, with an NRMSE never lower than 19.6%. Results show that NASA POWER can be useful for the generation of weather data sets where ground weather stations data is of missing or unavailable.

scholarly journals Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 303
Author(s):  
Eloise S. Fogarty ◽  
David L. Swain ◽  
Greg M. Cronin ◽  
Luis E. Moraes ◽  
Derek W. Bailey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Field Trials ◽  
Satellite System ◽  
False Positives ◽  
Sensor Data ◽  
Weather Data ◽  
Integrated Sensor ◽  
Detection Model ◽  
Independent Field ◽  
Grazing Sheep

In the current study, a simulated online parturition detection model is developed and reported. Using a machine learning (ML)-based approach, the model incorporates data from Global Navigation Satellite System (GNSS) tracking collars, accelerometer ear tags and local weather data, with the aim of detecting parturition events in pasture-based sheep. The specific objectives were two-fold: (i) determine which sensor systems and features provide the most useful information for lambing detection; (ii) evaluate how these data might be integrated using ML classification to alert to a parturition event as it occurs. Two independent field trials were conducted during the 2017 and 2018 lambing seasons in New Zealand, with the data from each used for ML training and independent validation, respectively. Based on objective (i), four features were identified as exerting the greatest importance for lambing detection: mean distance to peers (MDP), MDP compared to the flock mean (MDP.Mean), closest peer (CP) and posture change (PC). Using these four features, the final ML was able to detect 27% and 55% of lambing events within ±3 h of birth with no prior false positives. If the model sensitivity was manipulated such that earlier false positives were permissible, this detection increased to 91% and 82% depending on the requirement for a single alert, or two consecutive alerts occurring. To identify the potential causes of model failure, the data of three animals were investigated further. Lambing detection appeared to rely on increased social isolation behaviour in addition to increased PC behaviour. The results of the study support the use of integrated sensor data for ML-based detection of parturition events in grazing sheep. This is the first known application of ML classification for the detection of lambing in pasture-based sheep. Application of this knowledge could have significant impacts on the ability to remotely monitor animals in commercial situations, with a logical extension of the information for remote monitoring of animal welfare.

scholarly journals A method for simulating risk profiles of wheat yield in data-sparse conditions

2021 ◽  
pp. 1-12
Author(s):  
G. Bracho-Mujica ◽  
P.T. Hayman ◽  
V.O. Sadras ◽  
B. Ostendorf
Keyword(s):  
Reference Site ◽  
Climate Impacts ◽  
Weather Data ◽  
Climate Data ◽  
High Quality ◽  
Risk Profiles ◽  
Record Length ◽  
Daily Weather Data ◽  
Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

scholarly journals Dimension Analysis-Based Model for Prediction of Shale Compressive Strength

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xiangyu Fan ◽  
Fenglin Xu ◽  
Lin Chen ◽  
Qiao Chen ◽  
Zhiwei Liu ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Prediction Error ◽  
Diameter Ratio ◽  
Nonlinear Regression Model ◽  
Average Prediction Error ◽  
Model Average ◽  
Dimension Analysis ◽  
Longmaxi Shale

The compressive strength of shale is a comprehensive index for evaluating the shale strength, which is linked to shale well borehole stability. Based on correlation analysis between factors (confining stress, height/diameter ratio, bedding angle, and porosity) and shale compressive strength (Longmaxi Shale in Sichuan Basin, China), we develop a dimension analysis-based model for prediction of shale compressive strength. A nonlinear-regression model is used for comparison. A multitraining method is used to achieve reliability of model prediction. The results show that, compared to a multi-nonlinear-regression model (average prediction error = 19.5%), the average prediction error of the dimension analysis-based model is 19.2%. More importantly, our dimension analysis-based model needs to determine only one parameter, whereas the multi-nonlinear-regression model needs to determine five. In addition, sensitivity analysis shows that height/diameter ratio has greater sensitivity to compressive strength than other factors.

Advances in the data rescue and digitization of historical wind speed observations in Sweden: the WINDGUST project

2021 ◽  
Author(s):  
Erik Engström ◽  
Cesar Azorin-Molina ◽  
Lennart Wern ◽  
Sverker Hellström ◽  
Christophe Sturm ◽  
...  
Keyword(s):  
Climate Change ◽  
Wind Speed ◽  
Global Climate ◽  
Current Status ◽  
Weather Data ◽  
Climate Data ◽  
Intergovernmental Panel ◽  
Staff Members ◽  
The 1960S

&lt;p&gt;Here we present the progress of the first work package (WP1) of the project &amp;#8220;Assessing centennial wind speed variability from a historical weather data rescue project in Sweden&amp;#8221; (WINDGUST), funded by FORMAS &amp;#8211; A Swedish Research Council for Sustainable Development (ref. 2019-00509); previously introduced in EGU2019-17792-1 and EGU2020-3491. In a global climate change, one of the major uncertainties on the causes driving the climate variability of winds (i.e., the &amp;#8220;stilling&amp;#8221; phenomenon and the recent &amp;#8220;recovery&amp;#8221; since the 2010s) is mainly due to short availability (i.e., since the 1960s) and low quality of observed wind records as stated by the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC).&lt;/p&gt;&lt;p&gt;The WINDGUST is a joint initiative between the Swedish Meteorological and Hydrological Institute (SMHI) and the University of Gothenburg aimed at filling the key gap of short availability and low quality of wind datasets, and improve the limited knowledge on the causes driving wind speed variability in a changing climate across Sweden.&lt;/p&gt;&lt;p&gt;During 2020, we worked in WP1 to rescue historical wind speed series available in the old weather archives at SMHI for the 1920s-1930s. In the process we followed the &amp;#8220;Guidelines on Best Practices for Climate Data Rescue&amp;#8221; of the World Meteorological Organization. Our protocol consisted on: (i) designing a template for digitization; (ii) digitizing papers by an imaging process based on scanning and photographs; and (iii) typing numbers of wind speed data into the template. We will report the advances and current status, challenges and experiences learned during the development of WP1. Until new year 2020/2021 eight out of thirteen selected stations spanning over the years 1925 to 1948 have been scanned and digitized by three staff members of SMHI during 1,660 manhours.&lt;/p&gt;

Nondestructive Sampling to Monitor Macrophomina phaseolina Root Colonization in Overwintering Stevia

2021 ◽  
Author(s):  
Layne W. Rogers ◽  
Alyssa M. Koehler
Keyword(s):  
Root Colonization ◽  
Root Zone ◽  
Macrophomina Phaseolina ◽  
Field Trials ◽  
Soil Core ◽  
Climate Data ◽  
Sample Weight ◽  
The Family ◽  
Soil Temperatures

Macrophomina phaseolina is a soilborne fungal pathogen in the family Botryosphaeriaceae. Microsclerotia of M. phaseolina were first observed at the base of overwintering stevia stems in North Carolina in spring 2016. Previous studies utilized destructive sampling methods to monitor M. phaseolina in stevia fields; however, these methods are not feasible for long-term monitoring of disease in a perennial system. In the current study, nondestructive root soil-core sampling was conducted during overwintering months, from October 2018 to January 2020, to monitor M. phaseolina root colonization in stevia in Rocky Mount, NC. Two-inch-diameter soil cores were collected through the root zone, and fresh weight of roots was recorded for each soil core. M. phaseolina recovery was evaluated by examining mycelial growth from roots plated onto potato dextrose agar. There was no significant effect of sample weight on M. phaseolina across all dates, but there was one date for which sample weight had a significant effect on recovery (P = 0.01; α = 0.05). For both recovery and sample weight, sampling date was a significant predictor (P = 1.68e-5 and P = 0.0389, respectively; α = 0.05). Weather and climate data revealed that dates with no M. phaseolina recovery had lowest mean air and soil temperatures and the greatest number of days below freezing in the month prior to sampling. In separate sampling years, October sampling dates had the highest recovery of M. phaseolina. Future field trials should determine if October samplings can predict survival and vigor of reemerging stevia plants.

Capability of the Bayesian Forecasting Method to Predict Field Time Series

2018 ◽  
Vol 140 (12) ◽  
Author(s):  
Nicolò Gatta ◽  
Mauro Venturini ◽  
Lucrezia Manservigi ◽  
Giuseppe Fabio Ceschini ◽  
Giovanni Bechini
Keyword(s):  
Time Series ◽  
Prediction Error ◽  
Field Data ◽  
Single Step ◽  
Bayesian Forecasting ◽  
Virtual Sensors ◽  
Average Prediction Error ◽  
Prediction Scheme ◽  
Forecasting Method ◽  
Prediction Capability

This paper addresses the challenge of forecasting the future values of gas turbine measureable quantities. The final aim is the simulation of “virtual sensors” capable of producing statistically coherent measurements aimed at replacing anomalous observations discarded from the time series. Among the different available approaches, the Bayesian forecasting method (BFM) adopted in this paper uses the information held by a pool of observations as knowledge base to forecast the values at a future state. The BFM algorithm is applied in this paper to Siemens field data to assess its prediction capability, by considering two different approaches, i.e., single-step prediction (SSP) and multistep prediction (MSP). While SSP predicts the next observation by using true data as base of knowledge, MSP uses previously predicted data as base of knowledge to perform the prediction of future time steps. The results show that BFM single-step average prediction error can be very low, when filtered field data are analyzed. On the contrary, the average prediction error achieved in case of BFM multistep prediction is remarkably higher. To overcome this issue, the BFM single-step prediction scheme is also applied to clusters of time-wise averaged data. In this manner, an acceptable average prediction error can be achieved by considering clusters composed of 60 observations.

scholarly journals Variabilidade Espacial da Erosividade das Chuvas para o Estado da Paraíba (Variability of Spatial Erosivity of Rainfall in the Paraíba State)

2014 ◽  
Vol 7 (4) ◽  
pp. 691
Author(s):  
Bernardo Starling Dorta do Amaral ◽  
João Filadelfo de Carvalho Neto ◽  
Richarde Marques da Silva ◽  
José Carlos Dantas
Keyword(s):  
Water Resources ◽  
Rainfall Variability ◽  
Climatic Conditions ◽  
Climate Data ◽  
Rainfall Characteristics ◽  
R Factor ◽  
Erosivity Index ◽  
The Mean ◽  
Weather Stations ◽  
Soil Loss Equation

As características específicas das chuvas variam entre regiões, e o conhecimento da sua potencialidade erosiva é necessário para o planejamento dos recursos hídricos. Este estudo determinou a erosividade, analisou a variabilidade espacial da precipitação e o coeficiente de chuva para o Estado da Paraíba mediante técnicas de Sistemas de Informação Geográfica. Para a realização deste estudo foram utilizados dados climatológicos de 98 estações climatológicas da Embrapa, com séries de 1911 a 1990. Em seguida as informações sobre a erosividade foram processadas cartograficamente. O valor médio anual da erosividade das chuvas com base no índice EI30 para o Estado da Paraíba foi de 5.032,03 MJ.mm/ha/h, valor que representa o Fator “R” da Equação Universal de Perdas de Solo (USLE). As equações de regressão entre erosividade e precipitação e coeficiente de chuva não foram significativas. As principais conclusões são que: (a) os índices de erosividade encontrados são maiores na zona litorânea do que nas demais porções do Estado, e (b) as erosividades encontradas variaram de acordo com os valores da precipitação.   A B S T R A C T Specific rainfall characteristics vary among regions and their erosion potential must be known for the planning of water resources. This study analyzed the erosivity and rainfall variability and precipitation coefficient for Paraíba State based on Geographic Information Systems techniques. In order In this paper 98 climatological stations of Embrapa were used, with rainfall data of 1911 to 1990. For this study we use d climate data from 98 weather stations of Embrapa, with series from 1911 to 1990. Additionally we processed the information of the erosivity index cartographically by year and microregions. The mean annual value of erosivity was 5,032.03 MJ.mm/ha/h, which is to be used as “R” Factor in the Universal Soil Loss Equation (USLE) for Paraíba State and surrounding regions with similar climatic conditions. The main conclusions are that: (a) erosivity indexes are higher in coastal areas than in inland areas, and (b) the erosivity range according to the precipitation.   Keywords: erosivity, rainfall, water resources   

Cereal yield forecasting combining satellite drought-based indices, regional climate and weather data using machine learning approaches in Morocco

2021 ◽  
Author(s):  
El houssaine Bouras ◽  
Lionel Jarlan ◽  
Salah Er-Raki ◽  
Riad Balaghi ◽  
Abdelhakim Amazirh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Regional Climate ◽  
Model Development ◽  
Machine Learning Algorithms ◽  
Weather Data ◽  
Drought Indices ◽  
Support Vector ◽  
Learning Approaches ◽  
Climate Data ◽  
Yield Forecasting

&lt;p&gt;Cereals are the main crop in Morocco. Its production exhibits a high inter-annual due to uncertain rainfall and recurrent drought periods. Considering the importance of this resource to the country's economy, it is thus important for decision makers to have reliable forecasts of the annual cereal production in order to pre-empt importation needs. In this study, we assessed the joint use of satellite-based drought indices, weather (precipitation and temperature) and climate data (pseudo-oscillation indices including NAO and the leading modes of sea surface temperature -SST- in the mid-latitude and in the tropical area) to predict cereal yields at the level of the agricultural province using machine learning algorithms (Support Vector Machine -SVM-, Random forest -FR- and eXtreme Gradient Boost -XGBoost-) in addition to Multiple Linear Regression (MLR). Also, we evaluate the models for different lead times along the growing season from January (about 5 months before harvest) to March (2 months before harvest). The results show the combination of data from the different sources outperformed the use of a single dataset; the highest accuracy being obtained when the three data sources were all considered in the model development. In addition, the results show that the models can accurately predict yields in January (5 months before harvesting) with an R&amp;#178; = 0.90 and RMSE about 3.4 Qt.ha&lt;sup&gt;-1&lt;/sup&gt;. &amp;#160;When comparing the model&amp;#8217;s performance, XGBoost represents the best one for predicting yields. Also, considering specific models for each province separately improves the statistical metrics by approximately 10-50% depending on the province with regards to one global model applied to all the provinces. The results of this study pointed out that machine learning is a promising tool for cereal yield forecasting. Also, the proposed methodology can be extended to different crops and different regions for crop yield forecasting.&lt;/p&gt;

Sign in / Sign up

Export Citation Format

Share Document