Distribution level electric current consumption and meteorological data set of the East region of Paraguay

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.

Download Full-text

Harmonizing Multi-Source Remote Sensing Images for Summer Corn Growth Monitoring

Remote Sensing ◽

10.3390/rs11111266 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1266 ◽

Cited By ~ 3

Author(s):

Mingzheng Zhang ◽

Dehai Zhu ◽

Wei Su ◽

Jianxi Huang ◽

Xiaodong Zhang ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Kalman Filter ◽

Spatial Resolution ◽

Temporal Resolution ◽

Meteorological Data ◽

High Temporal Resolution ◽

Growth Monitoring ◽

Landsat 8 ◽

Data Set

Continuous monitoring of crop growth status using time-series remote sensing image is essential for crop management and yield prediction. The growing season of summer corn in the North China Plain with the period of rain and hot, which makes the acquisition of cloud-free satellite imagery very difficult. Therefore, we focused on developing image datasets with both a high temporal resolution and medium spatial resolution by harmonizing the time-series of MOD09GA Normalized Difference Vegetation Index (NDVI) images and 30-m-resolution GF-1 WFV images using the improved Kalman filter model. The harmonized images, GF-1 images, and Landsat 8 images were then combined and used to monitor the summer corn growth from 5th June to 6th October, 2014, in three counties of Hebei Province, China, in conjunction with meteorological data and MODIS Evapotranspiration Data Set. The prediction residuals ( Δ P R K ) in NDVI between the GF-1 observations and the harmonized images was in the range of −0.2 to 0.2 with Gauss distribution. Moreover, the obtained phenological curves manifested distinctive growth features for summer corn at field scales. Changes in NDVI over time were more effectively evaluated and represented corn growth trends, when considered in conjunction with meteorological data and MODIS Evapotranspiration Data Set. We observed that the NDVI of summer corn showed a process of first decreasing and then rising in the early growing stage and discuss how the temperature and moisture of the environment changed with the growth stage. The study demonstrated that the synthesized dataset constructed using this methodology was highly accurate, with high temporal resolution and medium spatial resolution and it was possible to harmonize multi-source remote sensing imagery by the improved Kalman filter for long-term field monitoring.

Download Full-text

The application of a neural network-based ragweed pollen forecast by the Ragweed Pollen Alarm System in the Pannonian biogeographical region

Aerobiologia ◽

10.1007/s10453-019-09615-w ◽

2019 ◽

Vol 36 (2) ◽

pp. 131-140 ◽

Cited By ~ 2

Author(s):

Z. Csépe ◽

Á. Leelőssy ◽

G. Mányoki ◽

D. Kajtor-Apatini ◽

O. Udvardy ◽

...

Keyword(s):

Neural Network ◽

Multilayer Perceptron ◽

Meteorological Data ◽

Ragweed Pollen ◽

Alarm System ◽

Good Tool ◽

Data Set ◽

The Neural Network ◽

Biogeographical Region ◽

Monitoring Stations

Abstract Ragweed Pollen Alarm System (R-PAS) has been running since 2014 to provide pollen information for countries in the Pannonian biogeographical region (PBR). The aim of this study was to develop forecast models of the representative aerobiological monitoring stations, identified by analysis based on a neural network computation. Monitoring stations with 7-day Hirst-type pollen trap having 10-year long validated data set of ragweed pollen were selected for the study from the PBR. Variables including forecasted meteorological data, pollen data of the previous days and nearby monitoring stations were used as input of the model. We used the multilayer perceptron model to forecast the pollen concentration. The multilayer perceptron (MLP) is a feedforward artificial neural network. MLP is a data-driven method to forecast the behaviour of complex systems. In our case, it has three layers, one of which is hidden. MLP utilizes a supervised learning technique called backpropagation for training to get better performance. By testing the neural network, we selected different sets of variables to predict pollen levels for the next 3 days in each of the monitoring stations. The predicted pollen level categories (low–medium–high–very high) are shown on isarithmic map. We used the mean square error, mean absolute error and correlation coefficient metrics to show the forecasting system’s performance. The average of the Pearson correlations is around 0.6 but shows big variability (0.13–0.88) among different locations. Model uncertainty is mainly caused by the limitation of the available input data and the variability in ragweed season patterns. Visualization of the results of the neural network forecast on isarithmic maps is a good tool to communicate pollen information to general public in the PBR.

Download Full-text

Relationship between MODIS based Aerosol Optical Depth and PM10 over Croatia

Open Geosciences ◽

10.2478/s13533-012-0135-6 ◽

2014 ◽

Vol 6 (1) ◽

Cited By ~ 8

Author(s):

Sanja Grgurić ◽

Josip Križan ◽

Goran Gašparac ◽

Oleg Antonić ◽

Zdravko Špirić ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Mass Concentration ◽

Meteorological Data ◽

Neural Network Approach ◽

Data Set ◽

Independent Variables ◽

Data Variability ◽

Modis Aod ◽

The Relationship

AbstractThis study analyzes the relationship between Aerosol Optical Depth (AOD) obtained from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and ground-based PM10 mass concentration distribution over a period of 5 years (2008–2012), and investigates the applicability of satellite AOD data for ground PM10 mapping for the Croatian territory. Many studies have shown that satellite AOD data are correlated to ground-based PM mass concentration. However, the relationship between AOD and PM is not explicit and there are unknowns that cause uncertainties in this relationship.The relationship between MODIS AOD and ground-based PM10 has been studied on the basis of a large data set where daily averaged PM10 data from the 12 air quality stations across Croatia over the 5 year period are correlated with AODs retrieved from MODIS Terra and Aqua. A database was developed to associate coincident MODIS AOD (independent) and PM10 data (dependent variable). Additional tested independent variables (predictors, estimators) included season, cloud fraction, and meteorological parameters — including temperature, air pressure, relative humidity, wind speed, wind direction, as well as planetary boundary layer height — using meteorological data from WRF (Weather Research and Forecast) model.It has been found that 1) a univariate linear regression model fails at explaining the data variability well which suggests nonlinearity of the AOD-PM10 relationship, and 2) explanation of data variability can be improved with multivariate linear modeling and a neural network approach, using additional independent variables.

Download Full-text

Soil, snow, weather, and sub-surface storage data from a mountain catchment in the rain–snow transition zone

Earth System Science Data Discussions ◽

10.5194/essdd-6-811-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 811-835 ◽

Cited By ~ 1

Author(s):

P. R. Kormos ◽

D. Marks ◽

C. J. Williams ◽

H. P. Marshall ◽

P. Aishlin ◽

...

Keyword(s):

Soil Moisture ◽

Transition Zone ◽

Soil Texture ◽

Soil Depth ◽

Meteorological Data ◽

Dew Point ◽

Soil Profiles ◽

Data Set ◽

Hydrologic Condition ◽

Wide Range

Abstract. A comprehensive hydroclimatic data set is presented for the 2011 water year to improve understanding of hydrologic processes in the rain-snow transition zone. This type of dataset is extremely rare in scientific literature because of the quality and quantity of soil depth, soil texture, soil moisture, and soil temperature data. Standard meteorological and snow cover data for the entire 2011 water year are included, which include several rain-on-snow events. Surface soil textures and soil depths from 57 points are presented as well as soil texture profiles from 14 points. Meteorological data include continuous hourly shielded, unshielded, and wind corrected precipitation, wind speed, air temperature, relative humidity, dew point temperature, and incoming solar and thermal radiation data. Sub-surface data included are hourly soil moisture data from multiple depths from 7 soil profiles within the catchment, and soil temperatures from multiple depths from 2 soil profiles. Hydrologic response data include hourly stream discharge from the catchment outlet weir, continuous snow depths from one location, intermittent snow depths from 5 locations, and snow depth and density data from ten weekly snow surveys. Though it represents only a single water year, the presentation of both above and below ground hydrologic condition makes it one of the most detailed and complete hydro-climatic datasets from the climatically sensitive rain-snow transition zone for a wide range of modeling and descriptive studies. Data are available at doi:10.1594/PANGAEA.819837.

Download Full-text

Climatology: Methods

Oxford Research Encyclopedia of African History ◽

10.1093/acrefore/9780190277734.013.27 ◽

2017 ◽

Author(s):

Sharon E. Nicholson

Keyword(s):

Oral Tradition ◽

Meteorological Data ◽

Climate Reconstruction ◽

Biological Information ◽

Data Set ◽

Historical Information ◽

Weather Events ◽

Climatic Record ◽

Government Records ◽

Types Of Information

Environmental constraints have large impacts on populations, especially in semi-arid regions such as Africa. Climate and weather have long affected African societies, but unfortunately the traditional climatic record for the continent is relatively short. For that reason, historical information has often been used to reconstruct climate of the past. Sources of historical information include reports and diaries of explorers, settlers, and missionaries; government records; reports of scientific expeditions; and historical geographical and meteorological journals. Local oral tradition is also useful. It is reported in the form of historical chronicles compiled centuries later. References to famine and drought, economic conditions, floods, agriculture, weather events, and the season cycle are examples of useful types of information. Some of the records also include meteorological measurements. More recently chemical and biological information, generally derived from lake cores, has been applied to historical climate reconstruction. Early works provided in most cases qualitative, discontinuous information, such as drought chronologies. However, a statistical method of climate reconstruction applied to a vast collection of historical information and meteorological data allowed for the creation of a two-century, semi-quantitative “precipitation” data set. It consists of annual indices related to rainfall since 1800 for ninety regions of the African continent. This data set has served to illustrate several 19th-century periods of anomalous rainfall conditions that affected nearly the entire continent. An example is widespread aridity during several decades early in that century.

Download Full-text

In situ ground based measurements of low level clouds during 10 years of Pallas cloud experiments.

10.5194/egusphere-egu2020-13138 ◽

2020 ◽

Author(s):

Konstantinos Doulgeris ◽

David Brus

Keyword(s):

Water Content ◽

Liquid Water ◽

Meteorological Data ◽

Liquid Water Content ◽

Data Set ◽

Radiative Balance ◽

Low Level ◽

Major Factors

Clouds and their interaction with aerosols are considered one of the major factors that are connected with uncertainties in predictions of climate change and are highly associated with earth radiative balance. Semi long term in-situ measurements of Arctic low-level clouds have been conducted during last 10 year (2009 - 2019) autumns at Sammaltunturi station (67&#9702;58&#180;N, 24&#9702;07&#180;E, and 560 m a.s.l.), the part of Pallas Atmosphere - Ecosystem Supersite and Global Atmosphere Watch (GAW) programme. During these years a unique data set of continuous and detailed ground-based cloud observations over the sub-Arctic area was obtained. The in-situ cloud measurements were made using two cloud probes that were installed on the roof of the station: the Cloud, Aerosol and Precipitation Spectrometer probe (CAPS) and the Forward Scattering Spectrometer Probe (FSSP), both made by droplet measurement technologies (DMT, Longmont, CO, USA). CAPS in&#173;cludes three instruments: the Cloud Imaging Probe (CIP, 12.5 &#956;m-1.55 mm), the Cloud and Aerosol Spectrometer (CAS-DPOL, 0.51-50 &#956;m) with depolarization feature and the Hotwire Liquid Water Content Sensor (Hotwire LWC, 0 - 3 g/m3). Vaisala FD12P weather sensor was used to measure all the meteorological data. The essential cloud microphysical parameters we investigated during this work were the size distributions, the total number concentrations, the effective radius of cloud droplets and the cloud liquid water content. The year to year comparison and correlations among semi long term in situ cloud measurements and meteorology are presented.

Download Full-text

EMO-5: Copernicus pan-European high-resolution meteorological data set for large-scale hydrological modelling

10.5194/egusphere-egu2020-21551 ◽

2020 ◽

Author(s):

Vera Thiemig ◽

Peter Salamon ◽

Goncalo N. Gomes ◽

Jon O. Skøien ◽

Markus Ziese ◽

...

Keyword(s):

High Resolution ◽

Real Time ◽

Large Scale ◽

Interpolation Method ◽

Meteorological Data ◽

Hydrological Modelling ◽

Data Set ◽

Average Temperature ◽

Time Period ◽

Meteorological Observations

We present EMO-5, a Pan-European high-resolution (5 km), (sub-)daily, multi-variable meteorological data set especially developed to the needs of an operational, pan-European hydrological service (EFAS; European Flood Awareness System). The data set is built on historic and real-time observations coming from 18,964 meteorological in-situ stations, collected from 24 data providers, and 10,632 virtual stations from four high-resolution regional observational grids (CombiPrecip, ZAMG - INCA, EURO4M-APGD and CarpatClim) as well as one global reanalysis product (ERA-Interim-land). This multi-variable data set covers precipitation, temperature (average, min and max), wind speed, solar radiation and vapor pressure; all at daily resolution and in addition 6-hourly resolution for precipitation and average temperature. The original observations were thoroughly quality controlled before we used the Spheremap interpolation method to estimate the variable values for each of the 5 x 5 km grid cells and their affiliated uncertainty. EMO-5 v1 grids covering the time period from 1990 till 2019 will be released as a free and open Copernicus product mid-2020 (with a near real-time release of the latest gridded observations in future). We would like to present the great potential EMO-5 holds for the hydrological modelling community.&#160;footnote: EMO = European Meteorological Observations

Download Full-text

NYTEFOX – The NY-Ålesund TurbulencE Fiber Optic eXperiment investigating the Arctic boundary layer, Svalbard

10.5194/essd-2021-37 ◽

2021 ◽

Author(s):

Marie-Louise Zeller ◽

Jannis-Michael Huss ◽

Lena Pfister ◽

Karl E. Lapo ◽

Daniela Littmann ◽

...

Keyword(s):

Boundary Layer ◽

Field Experiment ◽

Fiber Optic ◽

Meteorological Data ◽

Surface Radiation ◽

The Arctic ◽

Vertical Profiles ◽

Arctic Boundary Layer ◽

Data Set ◽

Sonic Anemometers

Abstract. The NY-Ålesund TurbulencE Fiber Optic eXperiment, NYTEFOX, was a field experiment at the Arctic site Ny-Ålesund (11.9° E, 78.9° N) and yielded a unique meteorological data set. These data describe the distribution of heat, airflows, and exchange in the Arctic boundary layer for a period of 14 days from 26 February to 10 March 2020. NYTEFOX is the first field experiment to investigate the heterogeneity of airflow and its transport in temperatures, wind, and kinetic energy in the Arctic environment using the Fiber-Optic Distributed Sensing (FODS) technique for horizontal and vertical observations. FODS air temperature and wind speed were observed at a spatial resolution of 0.127 m and 9 s in time along a horizontal array of 700 m at 1 m height above ground level (agl) and along three 7 m vertical profiles. Ancillary data were collected from three sonic anemometers and an acoustic profiler (miniSodar, SOund Detection And Ranging) yielding turbulent flow statistics and vertical profiles in the lowest 300 m agl, respectively. The observations from this field campaign are publicly available on Zenodo (https://doi.org/10.5281/zenodo.4335461) and supplement the data set operationally collected by the Basic Surface Radiation Network (BSRN) meteorological data set at Ny-Ålesund, Svalbard.

Download Full-text