scholarly journals Predicting Crop Evapotranspiration by Integrating Ground and Remote Sensors with Air Temperature Forecasts

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1740 ◽  
Author(s):  
Anna Pelosi ◽  
Paolo Villani ◽  
Salvatore Falanga Bolognesi ◽  
Giovanni Battista Chirico ◽  
Guido D’Urso
Keyword(s):  
Air Temperature ◽  
Irrigation Scheduling ◽  
Lead Times ◽  
Multispectral Images ◽  
Crop Evapotranspiration ◽  
Campania Region ◽  
Weather Forecasts ◽  
Advisory Systems ◽  
Crop Parameters ◽  
Main Component

Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where crop evapotranspiration (ETc) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ETc, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014–2015. ETc measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5th day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.

scholarly journals Probabilistic Forecasts Using Analogs in the Idealized Lorenz96 Setting

2011 ◽  
Vol 139 (6) ◽  
pp. 1960-1971 ◽  
Author(s):  
Jakob W. Messner ◽  
Georg J. Mayr
Keyword(s):  
Logistic Regression ◽  
Systematic Errors ◽  
Lead Times ◽  
Model Output ◽  
Weather Forecasts ◽  
Probabilistic Forecasts ◽  
Direct Model ◽  
Nwp Model

Abstract Three methods to make probabilistic weather forecasts by using analogs are presented and tested. The basic idea of these methods is that finding similar NWP model forecasts to the current one in an archive of past forecasts and taking the corresponding analyses as prediction should remove all systematic errors of the model. Furthermore, this statistical postprocessing can convert NWP forecasts to forecasts for point locations and easily turn deterministic forecasts into probabilistic ones. These methods are tested in the idealized Lorenz96 system and compared to a benchmark bracket formed by ensemble relative frequencies from direct model output and logistic regression. The analog methods excel at longer lead times.

scholarly journals Monthly Weather Forecasts in a Pest Forecasting Context: Downscaling, Recalibration, and Skill Improvement

2012 ◽  
Vol 51 (9) ◽  
pp. 1633-1638 ◽  
Author(s):  
Martin Hirschi ◽  
Christoph Spirig ◽  
Andreas P. Weigel ◽  
Pierluigi Calanca ◽  
Jörg Samietz ◽  
...  
Keyword(s):  
Insect Pest ◽  
Cydia Pomonella ◽  
Codling Moth ◽  
Lead Times ◽  
Practical Applications ◽  
Weather Forecasts ◽  
Weather Information ◽  
Agricultural Applications ◽  
Resampling Procedure ◽  
Skill Improvement

AbstractMonthly weather forecasts (MOFCs) were shown to have skill in extratropical continental regions for lead times up to 3 weeks, in particular for temperature and if weekly averaged. This skill could be exploited in practical applications for implementations exhibiting some degree of memory or inertia toward meteorological drivers, potentially even for longer lead times. Many agricultural applications fall into these categories because of the temperature-dependent development of biological organisms, allowing simulations that are based on temperature sums. Most such agricultural models require local weather information at daily or even hourly temporal resolution, however, preventing direct use of the spatially and temporally aggregated information of MOFCs, which may furthermore be subject to significant biases. By the example of forecasting the timing of life-phase occurrences of the codling moth (Cydia pomonella), which is a major insect pest in apple orchards worldwide, the authors investigate the application of downscaled weekly temperature anomalies of MOFCs for use in an impact model requiring hourly input. The downscaling and postprocessing included the use of a daily weather generator and a resampling procedure for creating hourly weather series and the application of a recalibration technique to correct for the original underconfidence of the forecast occurrences of codling moth life phases. Results show a clear skill improvement of up to 3 days in root-mean-square error over the full forecast range when incorporating MOFCs as compared with deterministic benchmark forecasts using climatological information for predicting the timing of codling moth life phases.

scholarly journals Evaluating sub-seasonal heatwave reforecasts of the ECMWF over Europe

2021 ◽  
Author(s):  
Natalia Korhonen ◽  
Otto Hyvärinen ◽  
Matti Kämäräinen ◽  
Kirsti Jylhä
Keyword(s):  
Heat Wave ◽  
Heat Waves ◽  
Lead Times ◽  
Weather Forecasts ◽  
Wave Forecast ◽  
Extended Range ◽  
Hot Days ◽  
Medium Range ◽  
Summer Temperatures

<p>Severe heatwaves have harmful impacts on ecosystems and society. Early warning of heat waves help with decreasing their harmful impact. Previous research shows that the Extended Range Forecasts (ERF) of the European Centre for Medium-Range Weather Forecasts (ECMWF) have over Europe a somewhat higher reforecast skill for extreme hot summer temperatures than for long-term mean temperatures. Also it has been shown that the reforecast skill of the ERFs of the ECMWF was strongly increased by the most severe heat waves (the European heatwave 2003 and the Russian heatwave 2010).</p><p>Our aim is to be able to estimate the skill of a heat wave forecast at the time the forecast is given. For that we investigated the spatial and temporal reforecast skill of the ERFs of the ECMWF to forecast hot days (here defined as a day on which the 5 days running mean surface temperature is above its summer 90<sup>th</sup> percentile) in the continental Europe in summers 2000-2019. We used the ECMWF 2-meter temperature reforecasts and verified them against the ERA5 reanalysis. The skill of the hot day reforecasts was estimated by the symmetric extremal dependence index (SEDI) which considers both hit rates and false alarm rates of the hot day forecasts. Further, we investigated the skill of the heatwave reforecasts based on at which time steps of the forecast the hot days were forecasted. We found that on the mesoscale (horizontal scale of ~500 km) the ERFs of the ECMWF were most skillful in predicting the life cycle of a heat wave (lasting up to 25 days) about a week before its start and during its course. That is, on the mesoscale those reforecasts, in which hot day(s) were forecasted to occur during the first 7…11 days, were more skillful on lead times up to 25 days than the rest of the heat wave forecasts. This finding is valuable information, e.g., in the energy and health sectors while preparing for a coming heat wave.</p><p>The work presented here is part of the research project HEATCLIM (Heat and health in the changing climate) funded by the Academy of Finland.</p>

scholarly journals Seasonal forecasts of the Saharan heat low characteristics: a multi-model assessment

2021 ◽  
Vol 2 (3) ◽  
pp. 893-912
Author(s):  
Cedric G. Ngoungue Langue ◽  
Christophe Lavaysse ◽  
Mathieu Vrac ◽  
Philippe Peyrillé ◽  
Cyrille Flamant
Keyword(s):  
West African Monsoon ◽  
Seasonal Forecast ◽  
Lead Times ◽  
Model Assessment ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Modes Of Variability ◽  
Forecast Models ◽  
Sahel Region ◽  
Heat Low

Abstract. The Saharan heat low (SHL) is a key component of the West African Monsoon system at the synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecast models. This is investigated using the latest versions of two seasonal forecast systems namely the SEAS5 and MF7 systems from the European Center of Medium-Range Weather Forecasts (ECMWF) and Météo-France respectively. The SHL characteristics in the seasonal forecast models are assessed based on a comparison with the fifth ECMWF Reanalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing and the intensity of the SHL pulsations when compared to ERA5. SEAS5 and MF7 show a cool bias centered on the Sahara and a warm bias located in the eastern part of the Sahara respectively. Both models tend to underestimate the interannual variability in the SHL. Large discrepancies are found in the representation of extremes SHL events in the seasonal forecast models. These results are not linked to our choice of ERA5 as a reference, for we show robust coherence and high correlation between ERA5 and the Modern-Era Retrospective analysis for Research and Applications (MERRA). The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the yearly distribution of the SHL and the forecast scores. The results highlight the capacity of the models to represent the intraseasonal pulsations (the so-called east–west phases) of the SHL. We notice an overestimation of the occurrence of the SHL east phases in the models (SEAS5, MF7), while the SHL west phases are much better represented in MF7. In spite of an improvement in prediction score, the SHL-related forecast skills of the seasonal forecast models remain weak for specific variations for lead times beyond 1 month, requiring some adaptations. Moreover, the models show predictive skills at an intraseasonal timescale for shorter lead times.

scholarly journals Irrigation Optimization Under a Limited Water Supply by the Integration of Modern Approaches into Traditional Water Management on the Cotton Fields

2019 ◽  
Vol 11 (18) ◽  
pp. 2127
Author(s):  
Polinova ◽  
Salinas ◽  
Bonfante ◽  
Brook
Keyword(s):  
Water Management ◽  
Water Supply ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Irrigation Schedule ◽  
Irrigation Scheduling ◽  
Future Climate Change ◽  
Weather Forecasts ◽  
Red Edge ◽  
Cotton Fields

The ability to effectively develop agriculture with limited water resources is an important strategic objective to face future climate change and to achieve the Sustainable Development Goal 2 (SDG2) of the United Nations. Since new conditions increasingly point to a limited water supply, the aim of modern irrigation management is to be sure to maximize the crop yield and minimize water use. This study aims to explore the advantages of the traditional agronomic approach, agro-hydrological model and field feedback obtained by spectroscopy, to optimize irrigation water management in the example of a cotton field. The study was conducted for two summer growing seasons in 2015 and 2016 in Kibbutz Hazorea, near Haifa, Israel. The irrigation schedule was developed by farmers using weather forecasts and corrected by the results of field inspections. The Soil Water Atmosphere Plant (SWAP) model was applied to optimize seasonal water distribution based on different criteria (critical soil pressure head and allowable daily stress). A new optimization algorithm for irrigation schedules by weather forecasts and vegetation indices was developed and presented in this paper. A few indices related to physical parameters and plant health (Normalized Difference Vegetation Index, Red Edge Normalized Difference Vegetation Index, Modified Chlorophyll Absorption Ratio Index 2, and Photochemical Reflectance Index) were considered. Red Edge Normalized Difference Vegetation Index proves itself as a suitable parameter for monitoring crop state due to its clear-cut response to irrigation treatments and was introduced in the developed algorithm. The performance of the considered irrigation scheduling approaches was assessed by a simulation model application for cotton fields in 2016. The results show, that the irrigation schedule developed by farmers did not compensate for the absence of precipitation in spring, which led to long-term lack of water during crop development. The optimization developed by SWAP allows determining the minimal amount of water which ensures appropriate yield. However, this approach could not take into account the non-linear effect of the lack of water at specific phenological stages on the yield. The new algorithm uses the minimal sufficient seasonal amount of water obtained from SWAP optimization. The approach designed allows one to prevent critical stress in cotton and distribute water in conformity with agronomic practice.

scholarly journals Impacts of Climatic Change on Reference Crop Evapotranspiration across Different Climatic Zones of Ningxia at Multi-Time Scales from 1957 to 2018

2020 ◽  
Vol 2020 ◽  
pp. 1-23
Author(s):  
Ziyang Zhao ◽  
Hongrui Wang ◽  
Cheng Wang ◽  
Wangcheng Li ◽  
Hao Chen ◽  
...  
Keyword(s):  
Water Management ◽  
Time Scales ◽  
Air Temperature ◽  
Climatic Factors ◽  
Crop Evapotranspiration ◽  
Climatic Zones ◽  
Reference Crop Evapotranspiration ◽  
Short Period ◽  
The Impact ◽  
Reference Crop

The impact of global climate change on agroecosystems is growing, affecting reference crop evapotranspiration (ET0) and subsequent agricultural water management. In this study, the climate factors temporal trends, the spatiotemporal variation, and the climate driving factors of ET0 at different time scales were evaluated across the Northern Yellow River Irrigation Area (NYR), Central Arid Zone (CAZ), and Southern Mountain Area (SMA) of Ningxia based on 20 climatic stations’ daily data from 1957 to 2018. The results showed that the Tmean (daily mean air temperature), Tmax (daily maximum air temperature), and Tmin (daily minimum air temperature) all had increased significantly over the past 62 years, whilst RH (relative humidity), U2 (wind speed at 2 m height), and SD (sunshine duration) had significantly decreasing trends across all climatic zones. At monthly scale, the ET0 was mainly concentrated from April to September. And at annual and seasonal scales, the overall increasing trends were more pronounced in NX, NYR, and SMA, while CAZ was the opposite. For the spatial distribution, ET0 presented a trend of rising first and then falling at all time scales. The abrupt change point for climatic factors and ET0 series was obtained at approximately 1990 across all climatic zones, and the ET0 had a long period of 25a and a short period of 10a at annual scale, while it was 15a and 5a at seasonal scale. RH and Tmax were the most sensitive climatic factors at the annual and seasonal scales, while the largest contribution rates were Tmax and SD. This study not only is important for the understanding of ET0 changes but also provides the preliminary and elementary reference for agriculture water management in Ningxia.

scholarly journals Coupling a Bat Algorithm with XGBoost to Estimate Reference Evapotranspiration in the Arid and Semiarid Regions of China

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Yixiu Han ◽  
Jianping Wu ◽  
Bingnian Zhai ◽  
Yanxin Pan ◽  
Guomin Huang ◽  
...  
Keyword(s):  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Bat Algorithm ◽  
Irrigation Scheduling ◽  
Multivariate Adaptive Regression Splines ◽  
Accurate Estimation ◽  
Agricultural Irrigation ◽  
Semiarid Regions ◽  
Arid And Semiarid

Accurate estimation of reference evapotranspiration (ETo) is key to agricultural irrigation scheduling and water resources management in arid and semiarid areas. This study evaluates the capability of coupling a Bat algorithm with the XGBoost method (i.e., the BAXGB model) for estimating monthly ETo in the arid and semiarid regions of China. Meteorological data from three stations (Datong, Yinchuan, and Taiyuan) during 1991–2015 were used to build the BAXGB model, the multivariate adaptive regression splines (MARS), and the gaussian process regression (GPR) model. Six input combinations with different sets of meteorological parameters were applied for model training and testing, which included mean air temperature (Tmean), maximum air temperature (Tmax), minimum air temperature (Tmin), wind speed (U), relative humidity (RH), and solar radiation (Rs) or extraterrestrial radiation (Ra, MJ m−2·d−1). The results indicated that BAXGB models (RMSE = 0.114–0.412 mm·d−1, MAE = 0.087–0.302 mm·d−1, and R2 = 0.937–0.996) were more accurate than either MARS (RMSE = 0.146–0.512 mm·d−1, MAE = 0.112–0.37 mm·d−1, and R2 = 0.935–0.994) or GPR (RMSE = 0.289–0.714 mm·d−1, MAE = 0.197–0.564 mm·d−1, and R2 = 0.817–0.980) model for estimating ETo. Findings of this study would be helpful for agricultural irrigation scheduling in the arid and semiarid regions and may be used as reference in other regions where accurate models for improving local water management are needed.

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

scholarly journals The Madden–Julian Oscillation and Boreal Winter Forecast Skill: An Analysis of NCEP CFSv2 Reforecasts

2015 ◽  
Vol 28 (15) ◽  
pp. 6297-6307 ◽  
Author(s):  
Charles Jones ◽  
Abheera Hazra ◽  
Leila M. V. Carvalho
Keyword(s):  
Forecast Skill ◽  
Western Pacific ◽  
Boreal Winter ◽  
Lead Times ◽  
Madden Julian Oscillation ◽  
Main Mode ◽  
Weather Forecasts ◽  
Synoptic Variability ◽  
The Impact ◽  
The Western Pacific

Abstract The Madden–Julian oscillation (MJO) is the main mode of tropical intraseasonal variations and bridges weather and climate. Because the MJO has a slow eastward propagation and longer time scale relative to synoptic variability, significant interest exists in exploring the predictability of the MJO and its influence on extended-range weather forecasts (i.e., 2–4-week lead times). This study investigates the impact of the MJO on the forecast skill in Northern Hemisphere extratropics during boreal winter. Several 45-day forecasts of geopotential height (500 hPa) from NCEP Climate Forecast System version 2 (CFSv2) reforecasts are used (1 November–31 March 1999–2010). The variability of the MJO expressed as different amplitudes, durations, and recurrence (i.e., primary and successive events) and their influence on forecast skill is analyzed and compared against inactive periods (i.e., null cases). In general, forecast skill during enhanced MJO convection over the western Pacific is systematically higher than in inactive days. When the enhanced MJO convection is over the Maritime Continent, forecasts are lower than in null cases, suggesting potential model deficiencies in accurately forecasting the eastward propagation of the MJO over that region and the associated extratropical response. In contrast, forecasts are more skillful than null cases when the enhanced convection is over the western Pacific and during long, intense, and successive MJO events. These results underscore the importance of the MJO as a potential source of predictability on 2–4-week lead times.

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