scholarly journals Comparison of ERA5-Land and UERRA MESCAN-SURFEX Reanalysis Data with Spatially Interpolated Weather Observations for the Regional Assessment of Reference Evapotranspiration

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1669 ◽  
Author(s):  
Anna Pelosi ◽  
Fabio Terribile ◽  
Guido D’Urso ◽  
Giovanni Chirico
Keyword(s):  
Vapor Pressure Deficit ◽  
Reference Evapotranspiration ◽  
Reanalysis Data ◽  
Weather Data ◽  
Kriging Interpolation ◽  
Regional Assessment ◽  
Campania Region ◽  
Weather Observations ◽  
Validation Procedure ◽  
Weather Stations

Reanalysis data are being increasingly used as gridded weather data sources for assessing crop-reference evapotranspiration (ET0) in irrigation water-budget analyses at regional scales. This study assesses the performances of ET0 estimates based on weather data, respectively produced by two high-resolution reanalysis datasets: UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). The study is conducted in Campania Region (Southern Italy), with reference to the irrigation seasons (April–September) of years 2008–2018. Temperature, wind speed, vapor pressure deficit, solar radiation and ET0 derived from reanalysis datasets, were compared with the corresponding estimates obtained by spatially interpolating data observed by a network of 18 automatic weather stations (AWSs). Statistical performances of the spatial interpolations were evaluated with a cross-validation procedure, by recursively applying universal kriging or ordinary kriging to the observed weather data. ERA5-Land outperformed UMS both in weather data and ET0 estimates. Averaging over all 18 AWSs sites in the region, the normalized BIAS (nBIAS) was found less than 5% for all the databases. The normalized RMSE (nRMSE) for ET0 computed with E5L data was 17%, while it was 22% with UMS data. Both performances were not far from those obtained by kriging interpolation, which presented an average nRMSE of 14%. Overall, this study confirms that reanalysis can successfully surrogate the unavailability of observed weather data for the regional assessment of ET0.

scholarly journals Investigation into the Effects of Climate Change on Reference Evapotranspiration Using the HadCM3 and LARS-WG

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 666 ◽  
Author(s):  
Maryam Bayatvarkeshi ◽  
Binqiao Zhang ◽  
Rojin Fasihi ◽  
Rana Muhammad Adnan ◽  
Ozgur Kisi ◽  
...  
Keyword(s):  
Climate Change ◽  
Reference Evapotranspiration ◽  
Irrigation Scheduling ◽  
Weather Data ◽  
Base Period ◽  
Resources Management ◽  
Weather Information ◽  
Daily Weather Data ◽  
Weather Stations ◽  
A1b Scenario

This study evaluates the effect of climate change on reference evapotranspiration (ET0), which is one of the most important variables in water resources management and irrigation scheduling. For this purpose, daily weather data of 30 Iranian weather stations from 1981 and 2010 were used. The HadCM3 statistical model was applied to report the output subscale of LARS-WG and to predict the weather information by A1B, A2, and B1 scenarios in three periods: 2011–2045, 2046–2079, and 2080–2113. The ET0 values were estimated by the Ref-ET software. The results indicated that the ET0 will rise from 2011 to 2113 approximately in all stations under three scenarios. The ET0 changes percentages in the A1B scenario during three periods from 2011 to 2113 were found to be 0.98%, 5.18%, and 12.17% compared to base period, respectively, while for the B1 scenario, they were calculated as 0.67%, 4.07%, and 6.61% and for the A2 scenario, they were observed as 0.59%, 5.35%, and 9.38%, respectively. Thus, the highest increase of the ET0 will happen from 2080 to 2113 under the A1B scenario; however, the lowest will occur between 2046 and 2079 under the B1 scenario. Furthermore, the assessment of uncertainty in the ET0 calculated by the different scenarios showed that the ET0 predicted under the A2 scenario was more reliable than the others. The spatial distribution of the ET0 showed that the highest ET0 amount in all scenarios belonged to the southeast and the west of the studied area. The most noticeable point of the results was that the ET0 differs from one scenario to another and from a period to another.

Enhanced capability to monitor drought using citizen weather stations

2021 ◽  
Author(s):  
Irene Garcia-Marti ◽  
Marijn de Haij ◽  
Hidde Leijnse ◽  
Jan Willem Noteboom ◽  
Aart Overeem ◽  
...  
Keyword(s):  
The Netherlands ◽  
Hydrological Cycle ◽  
Daily Rainfall ◽  
Rainfall Data ◽  
Added Value ◽  
Weather Data ◽  
Economic Sectors ◽  
Weather Observations ◽  
Drought Conditions ◽  
Weather Stations

<div> <div> <p>Recent studies indicate that global warming changes the global hydrological cycle and may trigger drought or expand and deepen existing drought conditions at our planet. During the summer of 2018 the Netherlands experienced extreme drought conditions, matching the previous drought record from 1976. This climatic extreme has been monitored using a cumulative metric based on the difference between (potential) evaporation and precipitation. In an effort to provide exhaustive drought monitoring facilities, the Netherlands Meteorological Service (KNMI) developed a drought monitor based on the Standard Precipitation Index (SPI) using 40 years of daily rainfall (1971-2010) from our official network of rain gauges for calibration. The daily SPI maps help decision makers to assess the status of meteorological drought in the Netherlands, thus enabling preventive measures mitigating its negative impacts on different socio-economic sectors. </p> </div> <div> <p>In the past two decades our global society has witnessed the advent of new technological and scientific advances that have reshaped the way we collect weather observations. Increasing numbers of citizens are joining the effort of monitoring the weather by installing citizen weather stations (CWS) in private spaces (e.g., home, schools), thus conforming novel sources of weather data. In 2015, the KNMI joined as a partner the Weather Observations Website (WOW) consortium, a citizen science initiative promoted by the UK Met Office bringing together weather enthusiasts all around the world. WOW-NL CWS have collected 100+ million observations between 2015-2019. However, it is still unclear how to use this remarkable volume of observations, or what is the added value (e.g., economic, operational, research) they provide with respect to the official network. </p> </div> <div> <p>In this ongoing work, we combined the newly developed SPI drought monitor with WOW observations from the Netherlands to obtain an ‘SPI-WOW’ indicator. Our goal is threefold: 1) illustrating how to turn WOW-NL data into operational value; 2) assessing the possibility of providing higher resolution drought maps including WOW-NL rainfall data; 3) enable the possibility for underrepresented regions to obtain (relevant) local drought metrics. </p> </div> <div> <p>We extracted 12 million precipitation observations for 2019 and, for each day of the year, we computed the daily rainfall accumulations for the previous 30 days (i.e., SPI-1). Note that the precipitation observations are not quality-controlled (QC). The calibrated model is tested with these newly created rainfall accumulations to obtain the SPI-WOW values. Our preliminary results compare the official vs alternative values of SPI at the location of each WOW-NL CWS. For each month we observe a moderate positive correlation, and there are CWS in the network capable of providing measurements close to the official ones. Further work to achieve the above-mentioned goal should include a) the application of a QC to the rainfall data to remove the outliers beforehand; b) thoroughly comparing the values of both networks in space and time across different scenarios; c) identifying the WOW-NL stations providing the best SPI metrics and its characteristics; d) assess the inclusion of radar data for the hi-res maps.</p> </div> </div>

scholarly journals Estimation of Daily Reference Evapotranspiration from NASA POWER Reanalysis Products in a Hot Summer Mediterranean Climate

Agronomy ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2077
Author(s):  
Gonçalo C. Rodrigues ◽  
Ricardo P. Braga
Keyword(s):  
Bias Correction ◽  
Reference Evapotranspiration ◽  
Estimation Method ◽  
Accurate Method ◽  
Weather Data ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Local Bias ◽  
Southern Portugal ◽  
Weather Stations

This study aims at assessing the accuracy of estimating daily reference evapotranspiration (ETo) computed with NASA POWER reanalysis products. Daily ETo estimated from local observations of weather variables in 14 weather stations distributed across Alentejo Region, Southern Portugal were compared with ETo derived from NASA POWER weather data, using raw and bias-corrected datasets. Three different methods were used to compute ETo: (a) FAO Penman-Monteith (PM); (b) Hargreaves-Samani (HS); and (c) MaxTET. Results show that, when using raw NASA POWER datasets, a good accuracy between the observed ETo and reanalysis ETo was observed in most locations (R2 > 0.70). PM shows a tendency to over-estimating ETo with an RMSE as high as 1.41 mm d−1, while using a temperature-based ET estimation method, an RMSE lower than 0.92 mm d−1 is obtained. If a local bias correction is adopted, the temperature-based methods show a small over or underestimation of ETo (–0.40 mm d−1≤ MBE < 0.40 mm d−1). As for PM, ETo is still underestimated for 13 locations (MBE < 0 mm d−1) but with an RMSE never higher than 0.77 mm d−1. When NASA POWER raw data is used to estimate ETo, HS_Rs proved the most accurate method, providing the lowest RMSE for half the locations. However, if a data regional bias correction is used, PM leads to the most accurate ETo estimation for half the locations; also, when a local bias correction is performed, PM proved the be the most accurate ETo estimation method for most locations. Nonetheless, MaxTET proved to be an accurate method; its simplicity may prove to be successful not only when only maximum temperature data is available but also due to the low data required for ETo estimation.

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 Evaluation of the Performance of CFSR Reanalysis Data Set for Estimating Potential Evapotranspiration (PET) in Turkey

2021 ◽  
Author(s):  
AHMET IRVEM ◽  
Mustafa OZBULDU
Keyword(s):  
Potential Evapotranspiration ◽  
Meteorological Data ◽  
Reanalysis Data ◽  
Actual Evapotranspiration ◽  
Observation Data ◽  
Reanalysis Dataset ◽  
Data Set ◽  
Climate Forecast System Reanalysis ◽  
Mountainous Regions ◽  
Weather Stations

Abstract Evapotranspiration is an important parameter for hydrological, meteorological and agricultural studies. However, the calculation of actual evapotranspiration is very challenging and costly. Therefore, Potential Evapotranspiration (PET) is typically calculated using meteorological data to calculate actual evapotranspiration. However, it is very difficult to get complete and accurate data from meteorology stations in, rural and mountainous regions. This study examined the availability of the Climate Forecast System Reanalysis (CFSR) reanalysis data set as an alternative to meteorological observation stations in the computation of potential annual and seasonal evapotranspiration. The PET calculations using the CFSR reanalysis dataset for the period 1987-2017 were compared to data observed at 259 weather stations observed in Turkey. As a result of the assessments, it was determined that the seasons in which the CFSR reanalysis data set had the best prediction performance were the winter (C'= 0.76 and PBias = -3.77) and the autumn (C' = 0.75 and PBias = -12.10). The worst performance was observed for the summer season. The performance of the annual prediction was determined as C'= 0.60 and PBias = -15.27. These findings indicate that the results of the PET calculation using the CFSR reanalysis data set are relatively successful for the study area. However, the data should be evaluated with observation data before being used especially in the summer models.

scholarly journals Comparison of meteorological data, related to reference evapotranspiration, from conventional and automatic stations in the Sertão and Agreste regions of Pernambuco, Brazil

DYNA ◽  
2021 ◽  
Vol 88 (216) ◽  
pp. 176-183
Author(s):  
Iug Lopes ◽  
Miguel Julio Machado Guimarães ◽  
Juliana Maria Medrado de Melo ◽  
Ceres Duarte Guedes Cabral de Almeida ◽  
Breno Lopes ◽  
...  
Keyword(s):  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Concordance Index ◽  
Good Relationship ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Scale ◽  
Weather Stations ◽  
Meteorological Elements

The objective was to perform a comparative study of the meteorological elements data that most cause changes in the reference Evapotranspiration (ETo, mm) and its own value, of automatic weather stations AWS and conventional weather stations CWS of the Sertão and Agreste regions of Pernambuco State. The ETo was calculated on a daily scale using the standard method proposed by the Food and Agriculture Organization (FAO), Penman-Monteith (FAO-56). The ETo information obtained from AWS data can be used to update the weather database of stations, since there is a good relationship between the ETo data obtained from CWS and AWS, statistically determined by the Willmott's concordance index (d > 0.7). The observed variations in the weather elements: air temperature, relative humidity, wind speed, and global solar radiation have not caused significant changes in the ETo calculation.

An Open-Sourced Web Application for Aerial Applicators to Avoid Spray Drift Caused by Temperature Inversion

2021 ◽  
Vol 37 (1) ◽  
pp. 77-84
Author(s):  
Yanbo Huang ◽  
D. K. Fisher
Keyword(s):  
Open Source ◽  
Web Application ◽  
Mississippi Delta ◽  
Temperature Inversion ◽  
Weather Data ◽  
List Type ◽  
Spray Drift ◽  
Atmospheric Conditions ◽  
Weather Stations ◽  
Open Source Hardware

HighlightsA web application for guiding data calculated from distributed weather data through open-source cloud service.A design scheme of portable weather stations built from inexpensive open-source electronics.Integration of open-source hardware and software for online guiding data to avoid drift caused by temperature inversion.Abstract. It is important for agricultural chemical applicators to follow proper spray procedures to prevent susceptible crops, animals, people, or other living organisms from being injured far downwind. Spraying during stable atmospheric conditions should be avoided to prevent surface-temperature inversion-induced off-target drift of crop protection materials. Previous statistical analysis determined times of high likelihood of stable atmospheric conditions, which are unfavorable for spraying, during the day under clear and cloudy conditions in hot summer months in the Mississippi Delta. Results validated the thresholds of temperature increase in the morning and temperature drop in the afternoon with wind speeds and the transition between stable and unstable atmospheric conditions. With this information, an algorithm was developed to calculate if atmospheric conditions were favorable for spraying based on field temperature and wind speed at any instant. With this algorithm, a web application was built to provide real-time determination of atmospheric stability and hourly online recommendation of whether aerial applications were appropriate for a location and time in the Mississippi Delta. This study further developed another web application specifically for Stoneville, Mississippi, with data measured from weather stations constructed from inexpensive open-source electronics, accessories, and software for more accurate online guidance for site-specific drift management. The web application is adapted for accessing on mobile terminals, such as smartphones and tablets, and provides timely guidance for aerial applicators and producers to avoid spray drift and air quality issues long distances downwind in the area. Keywords: Open-source hardware, Open-source software, Spray drift, Temperature inversion, Web application.

Comparison of snow cover characteristics according to weather stations and ERA 5-Land reanalysis in the Perm region

2021 ◽  
Vol 2 ◽  
pp. 95-110
Author(s):  
A.D., Kryuchkov ◽  
◽  
N.A Kalinin ◽  
Keyword(s):  
Snow Cover ◽  
Interannual Variability ◽  
Seasonal Variability ◽  
Snow Depth ◽  
Reanalysis Data ◽  
Error Distribution ◽  
Current Version ◽  
Weather Stations ◽  
Perm Region

Comparison of snow cover characteristics according to weather stations and ERA 5-Land reanalysis in the Perm region / Kryuchkov A.D., Kalinin N.A. // Hydrometeorological Research and Forecasting, 2021, no. 2 (380), pp. 95-110. The consistency of information on the snow depth contained in the ERA 5-Land reanalysis with data of weather stations of the Perm region is analyzed. The study is performed for the period from October 1990 to May 2020. It is shown that the interannual variability of the snow cover is generally successfully reflected by the current version of the reanalysis. Data on the snow availability are more accurately reproduced during the period of formation of the snow cover than during its melt. The performed calculations demonstrate a systematic overestimation of the snow depth in the ERA 5-Land reanalysis relative to the actual observations and a predominantly meridional error distribution on the territory of the Perm region. The maximum values in the seasonal variability of the snow cover occur earlier in the reanalysis than in the actual observations. Keywords: snow cover, reanalysis, weather stations, seasonal variability, interannual variability

scholarly journals Seasonal Time Series Forecasting by F1-Fuzzy Transform

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3611 ◽  
Author(s):  
Di Martino ◽  
Sessa
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Prediction Method ◽  
Heat Index ◽  
Weather Data ◽  
Fuzzy Transform ◽  
Campania Region ◽  
Seasonal Time Series ◽  
Forecasting Method ◽  
Daily Weather Data

We present a new seasonal forecasting method based on F1-transform (fuzzy transform of order 1) applied on weather datasets. The objective of this research is to improve the performances of the fuzzy transform-based prediction method applied to seasonal time series. The time series’ trend is obtained via polynomial fitting: then, the dataset is partitioned in S seasonal subsets and the direct F1-transform components for each seasonal subset are calculated as well. The inverse F1-transforms are used to predict the value of the weather parameter in the future. We test our method on heat index datasets obtained from daily weather data measured from weather stations of the Campania Region (Italy) during the months of July and August from 2003 to 2017. We compare the results obtained with the statistics Autoregressive Integrated Moving Average (ARIMA), Automatic Design of Artificial Neural Networks (ADANN), and the seasonal F-transform methods, showing that the best results are just given by our approach.

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