scholarly journals Data validation procedures in agricultural meteorology – a prerequisite for their use

2011 ◽  
Vol 6 (1) ◽  
pp. 141-146 ◽  
Author(s):  
J. Estévez ◽  
P. Gavilán ◽  
A. P. García-Marín
Keyword(s):  
Quality Control ◽  
Crop Production ◽  
Dynamic Range ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Irrigated Agriculture ◽  
Data Validation ◽  
Control Procedures ◽  
Weather Stations ◽  
Accurate Quantification

Abstract. Quality meteorological data sources are critical to scientists, engineers, climate assessments and to make climate related decisions. Accurate quantification of reference evapotranspiration (ET0) in irrigated agriculture is crucial for optimizing crop production, planning and managing irrigation, and for using water resources efficiently. Validation of data insures that the information needed is been properly generated, identifies incorrect values and detects problems that require immediate maintenance attention. The Agroclimatic Information Network of Andalusia at present provides daily estimations of ET0 using meteorological information collected by nearly of one hundred automatic weather stations. It is currently used for technicians and farmers to generate irrigation schedules. Data validation is essential in this context and then, diverse quality control procedures have been applied for each station. Daily average of several meteorological variables were analysed (air temperature, relative humidity and rainfall). The main objective of this study was to develop a quality control system for daily meteorological data which could be applied on any platform and using open source code. Each procedure will either accept the datum as being true or reject the datum and label it as an outlier. The number of outliers for each variable is related to a dynamic range used on each test. Finally, geographical distribution of the outliers was analysed. The study underscores the fact that it is necessary to use different ranges for each station, variable and test to keep the rate of error uniform across the region.

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.

scholarly journals Quality control procedures at Euskalmet data center

2012 ◽  
Vol 8 (1) ◽  
pp. 129-134 ◽  
Author(s):  
R. Hernández ◽  
M. Maruri ◽  
K. Otxoa de Alda ◽  
J. Egaña ◽  
S. Gaztelumendi
Keyword(s):  
Quality Control ◽  
Quality Assurance ◽  
Data Center ◽  
Basque Country ◽  
Automatic Quality Control ◽  
Control Procedures ◽  
Weather Stations ◽  
Indispensable Tool ◽  
Automated Software

Abstract. The Basque Country Mesonet measures more than 130 000 observations daily from its 85 Automatic Weather Stations (AWS). It becomes clear that automated software is an indispensable tool for quality assurance (QA) of this mesoscale surface observing network. This work describes a set of experimental semi-automatic quality control (QC) routines that is applied at Euskalmet data center. It has paid special attention to level validation design and associated flags, as well as to the system outputs, which are used by meteorologist and maintenance staff.

scholarly journals CrowdQC+—A Quality-Control for Crowdsourced Air-Temperature Observations Enabling World-Wide Urban Climate Applications

2021 ◽  
Vol 9 ◽  
Author(s):  
Daniel Fenner ◽  
Benjamin Bechtel ◽  
Matthias Demuzere ◽  
Jonas Kittner ◽  
Fred Meier
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
World Wide ◽  
Meteorological Data ◽  
Urban Climate ◽  
Specific Humidity ◽  
Data Sets ◽  
User Friendliness ◽  
Weather Stations ◽  
Further Development

In recent years, the collection and utilisation of crowdsourced data has gained attention in atmospheric sciences and citizen weather stations (CWS), i.e., privately-owned weather stations whose owners share their data publicly via the internet, have become increasingly popular. This is particularly the case for cities, where traditional measurement networks are sparse. Rigorous quality control (QC) of CWS data is essential prior to any application. In this study, we present the QC package “CrowdQC+,” which identifies and removes faulty air-temperature (ta) data from crowdsourced CWS data sets, i.e., data from several tens to thousands of CWS. The package is a further development of the existing package “CrowdQC.” While QC levels and functionalities of the predecessor are kept, CrowdQC+ extends it to increase QC performance, enhance applicability, and increase user-friendliness. Firstly, two new QC levels are introduced. The first implements a spatial QC that mainly addresses radiation errors, the second a temporal correction of the data regarding sensor-response time. Secondly, new functionalities aim at making the package more flexible to apply to data sets of different lengths and sizes, enabling also near-real time application. Thirdly, additional helper functions increase user-friendliness of the package. As its predecessor, CrowdQC+ does not require reference meteorological data. The performance of the new package is tested with two 1-year data sets of CWS data from hundreds of “Netatmo” CWS in the cities of Amsterdam, Netherlands, and Toulouse, France. Quality-controlled data are compared with data from networks of professionally-operated weather stations (PRWS). Results show that the new package effectively removes faulty data from both data sets, leading to lower deviations between CWS and PRWS compared to its predecessor. It is further shown that CrowdQC+ leads to robust results for CWS networks of different sizes/densities. Further development of the package could include testing the suitability of CrowdQC+ for other variables than ta, such as air pressure or specific humidity, testing it on data sets from other background climates such as tropical or desert cities, and to incorporate added filter functionalities for further improvement. Overall, CrowdQC+ could lead the way to utilise CWS data in world-wide urban climate applications.

scholarly journals Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018 ◽  
pp. 75
Author(s):  
D. Montero ◽  
F. Echeverry ◽  
F. Hernández
Keyword(s):  
Land Surface Temperature ◽  
Land Surface ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Meteorological Station ◽  
Significant Relation ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Digital Elevation ◽  
Weather Stations

<p>The Food and Agriculture Organization of the United Nations (FAO) in its publication No. 56 of the Irrigation and Drainage Series presents the FAO Penman-Monteith procedure for the estimation of reference evapotranspiration from meteorological data, however, its calculation may be complicated in areas where there are no weather stations. This paper presents an evaluation of the potential of the Land Surface Temperature and Digital Elevation Models products derived from the MODIS and ASTER sensors, both on board the Terra EOS AM-1 satellite, for the estimation of reference evapotranspiration using the Penman-Monteith FAO-56, Hargreaves, Thornthwaite and Blaney-Criddle models. The four models were compared with the method proposed by FAO calculated with the observed data of a ground based meteorological station, finding a significant relation with the models Penman-Monteith FAO-56 and Hargreaves.</p>

scholarly journals Missing data imputation of climate datasets: implications to modeling extreme drought events

2014 ◽  
Vol 29 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Gláucia Tatiana Ferrari ◽  
Vitor Ozaki
Keyword(s):  
Quality Control ◽  
Missing Data ◽  
Value Theory ◽  
The State ◽  
Agricultural Drought ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Control Procedures ◽  
Weather Stations ◽  
Agricultural Regions

Time series from weather stations in Brazil have several missing data, outliers and spurious zeroes. In order to use this dataset in risk and meteorological studies, one should take into account alternative methodologies to deal with these problems. This article describes the statistical imputation and quality control procedures applied to a database of daily precipitation from meteorological stations located in the State of Parana, Brazil. After imputation, the data went through a process of quality control to identify possible errors, such as: identical precipitation over seven consecutive days and precipitation values that differ significantly from the values in neighboring weather stations. Next, we used the extreme value theory to model agricultural drought, considering the maximum number of consecutive days with precipitation below 7 mm for the period between January and February, in the main soybean agricultural regions in the State of Parana.

scholarly journals Climate indicators for Italy: calculation and dissemination

2011 ◽  
Vol 6 (1) ◽  
pp. 147-150 ◽  
Author(s):  
F. Desiato ◽  
G. Fioravanti ◽  
P. Fraschetti ◽  
W. Perconti ◽  
A. Toreti
Keyword(s):  
Quality Control ◽  
Meteorological Data ◽  
Climate Trends ◽  
Regional Institutions ◽  
Climate Indicators ◽  
Database Structure ◽  
Wide Range ◽  
Rapid Dissemination ◽  
Control Procedures ◽  
Climate Studies

Abstract. In Italy, meteorological data necessary and useful for climate studies are collected, processed and archived by a wide range of national and regional institutions. As a result, the density of the stations, the length and frequency of the observations, the quality control procedures and the database structure vary from one dataset to another. In order to maximize the use of those data for climate knowledge and climate change assessments, a computerized system for the collection, quality control, calculation, regular update and rapid dissemination of climate indicators was developed. The products publicly available through a dedicated web site are described, as well as an example of climate trends estimates over Italy, based on the application of statistical models on climate indicators from quality-checked and homogenised time series.

Rescue of Ukrainian early historical climatological data

2020 ◽  
Author(s):  
Dmytro Boichuk ◽  
Jürg Luterbacher ◽  
Rob Allan ◽  
Olesya Skrynyk ◽  
Vladyslav Sidenko ◽  
...  
Keyword(s):  
Quality Control ◽  
Extreme Events ◽  
Meteorological Data ◽  
Hard Copy ◽  
Climate Services ◽  
Present Information ◽  
Control Procedures ◽  
Temperature Records ◽  
Meteorological Observations ◽  
The 19Th Century

&lt;p&gt;Modern climate applications and climate services are seeing the need for more data and information (including its historical part) on climate variability at high temporal and spatial resolution. Therefore, daily or even sub-daily meteorological data are required increasingly to feel this gap and provide the basis for climate research, extreme events analysis and impact studies.&lt;/p&gt;&lt;p&gt;The main objective of our work is to present information on results of data rescue (DARE) activity conducted recently in the Ukrainian Hydrometeorological Institute (UHMI, Kyiv, Ukraine) in close collaboration with several national and international partners. Our DARE activity was concentrated mainly on the original sub-daily, pre-1850 meteorological observations conducted at eight meteorological stations located in the territory of modern Ukraine, namely Kyiv, Kharkiv, Poltava, Kamyanets-Podilsky, Lugansk, Dnipro, Kherson and Odesa. These eight stations are the only ones, whose pre-1850 data have been found in an archive of the Central Geophysical Observatory (CGO), an observation institution of the Ukrainian Weather Service.&lt;/p&gt;&lt;p&gt;The data are contained in 38 special hard copy books. Before digitization, the book pages were photocopied to create a database of the images of all the paper sources. Its two copy versions are now stored at the UHMI and CGO, respectively. After the creation of the images database, the data were digitized manually by the authors. In total 291&amp;#160;103 values were digitized. These include 165&amp;#160;980 air temperature records (~57% of the total), 124&amp;#160;376 atmospheric pressure measurements (~42.7%) and 747 precipitation totals (~0.3%).&lt;/p&gt;&lt;p&gt;Quality control of the digitized data was conducted, including intercomparisons between the stations as well as comparisons with monthly temperature data that were digitized previously from other sources. The quality control procedures revealed a fairly good agreement among the rescued time series on the monthly time scale as well as a good accordance with the monthly data from other sources. However, several periods at some stations should be used with caution, due to relatively large discrepancies revealed. The rescued digital dataset can be used for different meteorological and climatological purposes, including the analysis of extreme events for the pre-1850 period in comparison with today&amp;#8217;s climate, regional climatological studies, etc. The dataset is an important supplement to existing digitized archives of meteorological measurements that were performed in the first half of the 19th century.&lt;/p&gt;

Modeling Reference Evapotranspiration in a Semiarid Area Using Data-driven Techniques: A Case Study in Lower Cheliff plain, Algeria

2021 ◽  
Author(s):  
Mohammed ACHITE ◽  
Muhammad Taghi Sattari ◽  
Abderrezak Kamel Toubal ◽  
Andrzej Wałęga ◽  
Nir Krakauer ◽  
...  
Keyword(s):  
Neural Network ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Global Radiation ◽  
Performance Criteria ◽  
Data Driven ◽  
Coefficient Of Determination ◽  
Irrigated Agriculture ◽  
Efficiency Coefficient

Abstract Evapotranspiration (ET) is an important part of the hydrologic cycle, especially when it comes to irrigated agriculture. For the estimation of reference evapotranspiration (ET0), direct methods either pose difficulties or call for many inputs that may not always be available from weather stations. This study compares Feed Forward Neural Network (FFNN), Radial Basis Function Neural Network (RBFNN). and Gene Expression Programming (GEP) approachs for the estimation of daily ET0 in a weather station in Lower Cheliff plain (northwest Algeria), over a 6-year period (2006–2011). Firstly, measured air temperature, relative humidity, wind speed, solar radiation and global radiation was used to calculate ET0 using FAO-56 Penman-Monteith equation as the reference. Then, the calculated ET0 using FAO-56 Penman-Monteith was considered as output for data driven models, while the measured meteorological data were considered as input of the models. The coefficient of determination (R2), root mean square error (RMSE) and Nash Sutcliffe efficiency coefficient (EF) were used to evaluate the developed models. The results of the developed models were compared with the Penman-Monteith evapotranspiration using these performance criteria. The FFNN model proved to yield the best performance compared to all the developed data-driven models, while the RBF-NN and GEP models also demonstrated potential for good performance.

scholarly journals Spatial and temporal variation in weather events critical for boreal agriculture: III Frost and winter time fluctuation

2016 ◽  
Vol 25 (1) ◽  
Author(s):  
Pirjo Peltonen-Sainio ◽  
Pentti Pirinen ◽  
Mikko Laapas ◽  
Hanna M. Mäkelä ◽  
Hannu Ojanen ◽  
...  
Keyword(s):  
Snow Cover ◽  
Crop Production ◽  
Soil Depth ◽  
Meteorological Data ◽  
Spatial And Temporal Variation ◽  
Daily Maximum ◽  
Significant Shift ◽  
Deep Soil ◽  
Soil Frost ◽  
Weather Stations

In the boreal zone of Europe, differences between the four seasons are considerable. Also, the within-season variation in climatic conditions is substantial. This has many impacts on agriculture that are exceptional when compared to any other environmental zone in Europe. All the meteorological data were based on weather observations made by the Finnish Meteorological Institute. Likelihood (%) for soil frost (≤ 0 °C at 20 cm soil depth) at nine weather stations, and late snow cover (> 1 cm) (10 km × 10 km grid) were estimated for late spring. Probabilities (%) of night frost at the ground surface (March-September) were calculated at nine weather stations by frequencies of the lowest observed night-time temperature: a) between –2 and –5 °C (mild), b) ≤ –5 °C (moderate) and c) ≤ –9 °C (severe). Also, the probabilities (%) of night frost in mid-summer were estimated (≤ –1 °C for at least five hours). Furthermore, a significant shift from mild to below-freezing conditions was measured in winter as a period of at least ten days with daily maximum temperatures above 0°C followed by at least a 10-day period with daily mean temperatures below –5°C in order to characterize high fluctuating winter conditions. All these except late snow cover constitute high risks to crop production. Deep soil frost may postpone sowings, while in advanced springs, night frost may cause damage. For winter crops and perennials, shifts from mild to cold spells outside the growing season are particularly detrimental. Again the data may have many other applications beyond the assessments highlighted in this paper.

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