Ground-based Weather Stations Research Articles

A Case Study on Deep Learning for Photovoltaic Power Forecasting Combining Satellite and Ground Data

The increasing demand for clean energy presents challenges in energy supply management, largely due to their intermittency. Photovoltaic power generation, in specific, is greatly affected by weather factors, which may render power grids susceptible to instability, quality and balance issues. In this context, photovoltaic power generation forecasting is crucial not only to enhance the management of diverse energy sources through generation planning, but also to ensure widespread adoption of photovoltaic energy. To address the predictability issue in generation, this study aims to investigate the combination of satellite data with meteorological data to predict the energy generation potential in photovoltaic panels within 30, 60, 120, and 180-minute horizons. For this purpose, images from the GOES-16 satellite are used in combination with data from a ground-based weather station, located at Florianópolis – Santa Catarina – Brazil. The data is fed to a convolutional neural network, where convolutions are employed to extract features from the satellite images, aiming to establish a relationship with solar irradiation. The output of the convolutional network serves as input for a multilayer perceptron network, which utilizes the data to predict the Global Horizontal Irradiance (GHI). Our results support that models incorporating satellite images provide forecasts approximately 41% better for the 30-minute horizon and 21% better for the 180-minute horizon, when compared to models without satellite images.

Monitoring meteorological drought in Hoa Vang district, Da Nang City, using Google Earth Engine and satellite-derived precipitation data combined with ground-based weather stations

Monitoring meteorological drought in Hoa Vang district, Da Nang City, using Google Earth Engine and satellite-derived precipitation data combined with ground-based weather stations

Local Detection of Ground Coupled Acoustic Waves with Seismic Arrays and Their Potential Role in the Discrimination of Explosions and Earthquakes

Abstract Acoustic waves are widely used to characterize explosive sources such as volcanoes, meteorites, and controlled explosions. This study examines the potential role of ground coupled airwaves (GCA), which effectively propagate at acoustic speeds (∼0.34 km/s) before coupling to the ground near seismometers, in aiding local discrimination between low-yield explosions in shallow boreholes and earthquakes. GCA generated by shallow borehole explosions from the 2014 imaging magma under St. Helens experiment (ML 0.9–2.3) and earthquakes (ML 2–3.4) from 2014 to 2016, were recorded by various seismometers at <150 km source–receiver distance. Potential GCA are analyzed using arrays of broadband seismometers (number of seismometers, n = 85), nodal seismometers with 10-Hz geophones atop the surface (n = 904), and Texan dataloggers with shallowly buried 4.5-Hz geophones (n = 2535). Array-based detections are defined using the distributions of short-time average over long-time average functions in time windows during and adjacent to the predicted GCA arrival for direct source–receiver transmission. GCA are detected for 14 of 23 borehole explosions and 0 of 34 earthquakes. All detections occurred during times of low-mean wind speed (<0.5 m/s) at ground-based weather stations. GCA amplitudes exhibit strong spatial variability, and the number of spatially distributed receivers appears more important for GCA detection than the type of seismometer installation. GCA detections were compared with seismic P/S amplitude ratios, which are a common source discriminant, and field logs of whether the borehole explosions ejected any mass or deformed the surface. No clear correlation was found with either type of source information, suggesting that heterogeneous propagation and near-receiver effects like wind noise are more influential than variations in source processes among the 23 explosions. Our results indicate that local seismic detection of GCA may valuably complement discrimination metrics like P/S ratios, with a low tendency for false-positive indications of explosions but a high tendency for false negatives.

Enhancing Precipitation Prediction in the Ziz Basin: A Comprehensive Review of Traditional and Machine Learning Approaches

Accurate precipitation forecasting is paramount for various sectors. Traditional methods for rainfall prediction involve understanding physical processes, historical weather data, and statistical models. These methods utilize observations from ground-based weather stations, satellites, and weather radars to assess current conditions and predict future precipitation. However, accurate precipitation prediction remains challenging due to the intricate and non-linear characteristics of rainfall. Over the past few years, machine learning (ML) algorithms have shown promise in improving precipitation prediction accuracy. This research provides an overview of both traditional methods and advanced ML models applicable to rainfall prediction, including regression, classification, and time series models. We conducted a comprehensive review of related works that explore the impact of using ML algorithms for rainfall estimation. Through this analysis, we identified the strengths and limitations of ML models in this context and highlighted advancements in rainfall prediction using these algorithms. We possess a comprehensive dataset, spanning data from 1996 to 2015, comprising historical weather data from the Ziz basin, our designated study area. This dataset contains five key meteorological features: precipitation, humidity, wind, temperature, and evaporation. In terms of perspective, we plan to utilize this dataset and conduct a comprehensive comparative study to evaluate the performance of different ML models. Our objective is to demonstrate the effectiveness and potential of these algorithms in improving weather forecasting capabilities and enhancing the accuracy of rainfall estimation methods in the specific study area.

Evaluating the freeze-thaw vulnerability of soapstone monuments and geoheritage sites in the Parco del Paradiso (Chiavenna, Italy).

This study investigates the vulnerability of soapstone monuments and ancient quarries in the Parco del Paradiso, Chiavenna, to freeze-thaw cycles. It employs climate-based damage functions to assess the risk of weathering-induced damage. A crucial aspect of the assessment involves the study of the homogeneity and trends in the selected climate data sources in the area of the case study. These data sources are ground-based weather stations, and in-situ installed temperature-humidity data loggers. The findings of this contribution underscore the significance of appropriately chosen reference climatic series and validation tests in ensuring the accuracy of the climate signal to analyze freeze-thaw statistics for specific cultural heritage sites. These insights are essential not only for supporting reliable assessment of the vulnerability of these invaluable heritage sites, which have played a vital role in local architecture and trade since Roman times, and for the successive development of long-term preventive conservation and maintenance strategies.

Validation of precipitation obtained from GPM and H-SAF satellite data with relation to Ukrainian ground weather stations' data

The main objective of this study is to validate H-SAF and GPM satellite precipitation data for the territory of Ukraine. Validation was carried out by comparing satellite data with data from ground weather stations of Ukrainian Hydrometeorological Center and included the following stages: data collection and processing, bringing the data to the same spatial and temporal resolution, calculation of correlation coefficients and construction of confusion matrices. Correlation coefficients were calculated for each of the stations for two studied periods (the first period - April-September 2020, the second period - April-September 2021). In addition, to ensure a more detailed analysis, confusion matrices were constructed based on division of precipitation into classes of different intensity. The comparative analysis showed that a larger number of stations (117) has a correlation coefficient of 0.5 - 0.8 with relation to GPM data, and, as for HSAF precipitation data, a larger number of stations (59) has a low correlation coefficient (0 - 0.3). Confusion matrices were calculated for the following four classes of rainfalls (mm): “0-2”, “2-5”, “5-10”, “>10”. The confusion matrices coefficients showed that low-intensity precipitation or “no precipitation”, as well as high-intensity precipitation, can be determined by a satellite with high accuracy as opposed to the ground-based weather station measurements. Thus, according to the "specificity" indicator, a precipitation class >10 mm has the highest level of correspondence of satellite data (H-SAF, GPM Late, GPM Early) to ground data. The low value of the "specificity" for the range of 0-2 mm is explained by the fact that satellite methods are able to detect very low values of precipitation intensity, while the station shows their absence – 0 mm. Thus, according to the values of the confusion matrices, we see that a large part of the values of ground measurements of precipitation in the range of 0-2 mm are shown by the satellite method as “2-5 mm” class. We can also see that the “2-5 mm” class by satellite has the most confusion with the 0-2 mm class by station (for H-SAF, GPM Late, GPM Early). But based on the high values of the "recall" for the range of 0-2 mm (for H-SAF, GPM Late, GPM Early), we can conclude that when the station does not show this amount of precipitation (0-2 mm), then the satellite also shows another class of values. The values of "precision" coefficient show that the satellite measurements are actually reliable. This means that presence or absence of precipitation may be determined precisely, since the highest value of the "precision" coefficient is typical for low and high amounts of precipitation (on average 0.76 and 0.66, respectively). Detected by satellites, heavy precipitation or no precipitation are also confirmed in most cases by ground stations. Based on the results of the validation, GPM and H-SAF satellite precipitation data products can be used for rainfall monitoring and considered as an additional source of meteorological information.

Refractivity Observations from Radar Phase Measurements: The 22 May 2002 Dryline Case during IHOP Project

The dryline, often associated with the development of severe storms in the Southern Great Plains of the United States of America, is a boundary layer phenomenon that occurs when a warm and moist air mass from the Gulf of Mexico meets a hot and dry air mass from the southwest desert area. An accurate knowledge of the water vapor spatio-temporal variability in the lower part of the atmosphere is crucial for a better understanding of the evolution of the dryline. The tropospheric refractivity, directly related to water vapor content, is a proxy for the water vapor content of the troposphere. It has already been demonstrated that the refractivity and the refractivity vertical gradient can be jointly estimated from radar phase measurements. In fact, it has been shown that using kriging interpolation techniques, accurate refractivity maps within the coverage area of the radar can be obtained with high temporal resolution. In this paper, a detailed analysis of the time series of radar-based refractivity maps obtained during a dryline that occurred on the afternoon of 22 May 2002 during the International H2O Project (IHOP_2002) is presented. Comparisons between the time series of radar refractivity maps, obtained with the NCAR S-Pol radar, and the refractivity measurements derived from automatic ground-based weather stations and the AERI instrument, placed at different locations within the coverage area of the NCAR S-Pol radar, demonstrate the accuracy of radar refractivity estimates even for highly variable conditions, both in time and space, in the troposphere. Correlation coefficients higher than 0.95 are obtained in all weather station locations. Regarding the RMSE, errors less than 6 N-units are obtained for all cases, being even as low as 2.92 N-units at some locations.

Performance of the Copernicus European Regional Reanalysis (CERRA) dataset as proxy of ground-based agrometeorological data

The continuous advances in numerical modeling of the atmosphere, computing power and data assimilation techniques entail frequent updates of numerical weather prediction (NWP) models that show improved forecast skill. This circumstance leads to the recurrent delivery of revised reanalysis databases that provide weather estimates for several decades back in time by combining the latest NWP models with observations. Since climate studies and agriculture water management applications require the availability of accurate and reliable weather data, assessing the performance of reanalysis products contributes to informed choices of potential weather proxy of ground-based agrometeorological data. CERRA (Copernicus European Regional ReAnalysis) dataset is the latest regional reanalysis product released by the European Centre for Medium-Range Weather Forecasts (ECMWF), in August 2022. CERRA is forced by the global ERA5 reanalysis, and it provides weather data with resolution of 5.5 km for the pan-European territory from 1984. For the first time in literature, this study explores the performance of CERRA data at 38 ground-based weather stations located in Sicily, an Italian region with Mediterranean climate, during the irrigation seasons 2003–2022. The objective of the study lies in evaluating CERRA performance with respect to air temperature, actual vapor pressure, wind speed and solar radiation that are input variables for assessing reference evapotranspiration, ETO, which is a key variable for quantifying irrigation volumes needed for water resources studies. The accuracy of ETO estimates depends on those input variables through the equation provided by the Food and Agriculture Organization of the United Nations (FAO), i.e., the FAO Penman-Monteith equation. Here, it is also evaluated the performance of ETO estimates by using CERRA weather inputs in the FAO Penman-Monteith equation. The results show that the performances of CERRA weather data are excellent, especially for air temperature, and this determines that CERRA ETO estimates present high accuracy and reliability with a mean PBIAS and NRMSE equal to 5.6% and 13%, respectively, over the region. Those outcomes lead to the conclusion that CERRA dataset represents a valid alternative to ground-based agrometeorological measurements and their spatial interpolation for water resource regional studies.

Grid-to-Point Deep-Learning Error Correction for the Surface Weather Forecasts of a Fine-Scale Numerical Weather Prediction System

Forecasts of numerical weather prediction models unavoidably contain errors, and it is a common practice to post-process the model output and correct the error for the proper use of the forecasts. This study develops a grid-to-multipoint (G2N) model output error correction scheme which extracts model spatial features and corrects multistation forecasts simultaneously. The model was tested for an operational high-resolution model system, the precision rapid update forecasting system (PRUFS) model, running for East China at 3 km grid intervals. The variables studied include 2 m temperature, 2 m relative humidity, and 10 m wind speed at 311 standard ground-based weather stations. The dataset for training G2N is a year of historical PRUFS model outputs and the surface observations of the same period and the assessment of the G2N performance are based on the output of two months of real-time G2N. The verification of the real-time results shows that G2N reduced RMSEs of the 2 m temperature, 2 m relative humidity, and 10 m wind speed forecast errors of the PRUFS model by 19%, 24%, and 42%, respectively. Sensitivity analysis reveals that increasing the number of the target stations for simultaneous correction helps to improve the model performance and reduces the computational cost as well indicating that enhancing the loss function with spatial regional meteorological structure is helpful. On the other hand, adequately selecting the size of influencing grid areas of the model input is also important for G2N to incorporate enough spatial features of model forecasts but not to include the information from the grids far from the correcting areas. G2N is a highly efficient and effective tool that can be readily implemented for real-time regional NWP models.

Phenotypic analysis of heat stress in Holsteins using test-day production records and NASA POWER meteorological data

Weather station data and test-day production records can be combined to quantify the effects of heat stress on production traits in dairy cattle. However, meteorological data sets that are retrieved from ground-based weather stations can be limited by spatial and temporal data gaps. The National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) database provides meteorological data over regions where surface measurements are sparse or nonexistent. The first aim of this study was to determine whether NASA POWER data are a viable alternative resource of weather data for studying heat stress in Canadian Holsteins. The results showed that average, minima, and maxima ambient temperature and dewpoint temperature as well as 4 different types of temperature-humidity index (THI) values from NASA POWER were highly correlated to the corresponding values from weather stations (regression R2 > 0.80). However, the NASA POWER values for the daily average, minima, and maxima wind speed and relative humidity were poorly correlated to the corresponding weather station values (regression R2 = 0.10 to 0.49). The second aim of this study was to quantify the influence of heat stress on Canadian dairy cattle. This was achieved by determining the THI values at which milk, protein, and fat yield started to decline due to heat stress as well as the rates of decline in these traits after the respective thresholds, using segmented polynomial regression models. This was completed for both primiparous and multiparous cows from 5 regions in Canada (Ontario, Quebec, British Columbia, the Prairies, and the Atlantic Maritime). The results showed that all production traits were negatively affected by heat stress and that the patterns of responses for milk, fat, and protein yields to increasing THI differed from each other. We found 3 THI thresholds for milk yield, 1 for fat yield, and 2 for protein yield. All thresholds marked a change in rate of decrease in production yield per unit THI, except for the first milk yield threshold, which marked a greater rate of increase. The first thresholds for milk yield ranged between 47 and 50, the second thresholds ranged between 61 and 69, and the third thresholds ranged between 72 and 76 THI units. The single THI threshold for fat yield ranged between 48 and 55 THI units. Finally, the first and second thresholds ranged between 58 and 62 THI units and 72 and 73 THI units for protein yield, respectively.

Remote sensing to assess the risk for cultural heritage: forecasting potential collapses due to rainfall in historic fortifications

PurposeHeavy rainfall is one of the main causes of the degradation of historic rammed Earth architecture. For this reason, ensuring the conservation thereof entails understanding the factors involved in these risk situations. The purpose of this study is to research three past events in which rainfall caused damage and collapse to historic rammed Earth fortifications in Andalusia in order to analyse whether it is possible to prevent similar situations from occurring in the future.Design/methodology/approachThe three case studies analysed are located in the south of Spain and occurred between 2017 and 2021. The hazard presented by rainfall within this context has been obtained from Art-Risk 3.0 (Registration No. 201999906530090). The vulnerability of the structures has been assessed with the Art-Risk 1 model. To characterise the strength, duration, and intensity of precipitation events, a workflow for the statistical use of GPM and GSMaP satellite resources has been designed, validated, and tested. The strength of the winds has been evaluated from data from ground-based weather stations.FindingsGSMaP precipitation data is very similar to data from ground-based weather stations. Regarding the three risk events analysed, although they occurred in areas with a torrential rainfall hazard, the damage was caused by non-intense rainfall that did not exceed 5 mm/hour. The continuation of the rainfall for several days and the poor state of conservation of the walls seem to be the factors that triggered the collapses that fundamentally affected the restoration mortars.Originality/valueA workflow applied to vulnerability and hazard analysis is presented, which validates the large-scale use of satellite images for past and present monitoring of heritage structure risk situations due to rain.

Reliability of the IMERG product through reference rain gauges in Central Italy

Satellite precipitation observations represent the future of climatology, as they allow continuous assessment of climate parameters over the entire earth's surface. Today, climate detection methods are in a transitional period where satellite data are not yet perfectly calibrated, while ground-based weather stations are still the most reliable detection methods. Precipitation is one of the most important climatic parameters, but at the same time it is complex to measure accurately by satellite. In this context it is essential to evaluate the reliability of a very useful and universally recognised satellite product, the IMERG V06 Final Run with validated weather stations in Central Italy. This could allow, on the one hand, to assess the accuracy of this satellite product compared to rain gauges, and on the other hand a better calibration at local scale. The analysis involved as many as 154 rain gauges in an area of about 12,600 Km2, so as to cover the area in a punctual way; in this context, two different comparison procedures were tested, pixel-to-point and pixel-to-pixel at annual and monthly scale, in the period 2001–2021. The results showed an overestimation of rainfall by the IMERG product, compared to rain gauges of about 200 mm per year, while in the coastal and hilly areas, IMERG's performance is even worse, overestimating precipitation by almost 1000 mm. This study showed that the pixel-to-pixel procedure proved to be the most reliable, showing the existence of a linear relationship between altitude and the differences between rain gauges and IMERG data, with an annual linear regression explaining up to 82% of the overall variance, allowing a possible calibration of the satellite product in the area.

ОПЫТ ИСКУССТВЕННОГО ВЫЗЫВАНИЯ ОСАДКОВ В ЦЕЛЯХ ТУШЕНИЯ ЛЕСНЫХ ПОЖАРОВ В СИБИРИ И НА ДАЛЬНЕМ ВОСТОКЕ В 2017-2021 ГГ.: ПРЕДВАРИТЕЛЬНЫЕ РЕЗУЛЬТАТЫ И ВОПРОСЫ ОЦЕНКИ ЭФФЕКТИВНОСТИ

Since 1968, the Aerial Forest Protection Service (Avialesookhrana) has taken measures to extinguish wildfires and reduce fire danger in adjacent areas by rainmaking with methods and technologies of precipitation enhancement developed at Roshydromet research institutions. The efficiency of the performed modification is analyzed in the absence of a network of Doppler weather radars, as well as in conditions of a sparse ground-based weather station network in most of Siberia and the Far East. Due to the significant spatial variability of precipitation, the floating control method was applied using data of numerical weather prediction models. The analysis of precipitation enhancement activities demonstrated a rather high efficiency of applying this method: the actual amount of precipitation was 1.4-1.5 times higher than the expected amount averaged according to the forecast models.

Assessment and synergy analysis of outdoor thermal comfort and thermal infrared remote sensing for urban heat island studies

The aim of this research is to explore the potentialities and limits of the integration of remote sensing biophysical data (land surface temperature) in the outdoor thermal comfort studies. Accordingly, by examining correlations between land surface temperature and air temperature, and using respectively remote sensing satellite data MODIS and different weather stations archives alongside questionnaire surveys. Currently, the parameters of thermal comfort indices are usually calculated using the data from one, or few permanent or portable ground-based weather stations. Due to the lack of adequate distribution of weather stations, those calculations generally do not accurately represent the alteration of thermal comfort, through time and space. Nevertheless, it has been essentially proved that despite strong tendencies between in-situ measured parameters and remotely sensed ones, various elements need to be studied (e.g., location, land surface type, vegetation, and elevation). Finally, preliminary results confirm that the proposed linear approaches are providing considerable and promising performance suitable for future specific situations and studies purposes.

Assessment of satellite-based MERRA climate data in AASHTOWare pavement mechanistic-empirical design

The pavement performance system depends on various factors including traffic loading, climate conditions, and material properties. Variations in ambient temperatures and moistures can change material properties and consequently affect the overall pavement performance. Collecting climate data has been a significant challenge. This study compares the pavement performance predictions of Iowa composite pavement systems in AASHTOWare Pavement ME Design (PMED) software using four climate data sources: (1) ground-based weather station, (2) North American Regional Reanalysis and (3) the Modern-Era Retrospective Analysis for Research and Applications (MERRA) version 1 and (4) MERRA version 2. The findings showed disagreement among climate data sources for some of the distresses due to the significant differences in percent sunshine hourly measurements. Database for ‘synthetic’ percent sunshine is developed using surface shortwave radiation (SSR) estimates directly provided by MERRA to improve pavement performance predictions. This study recommends the use of SSR as an input in PMED.

A Visual Support of Standard Procedures for Solar Radiation Quality Control

Solar irradiance data from high-quality ground-based measurements are primordial for different solar energy applications. In order to achieve the required accuracy, quality control procedures are of great benefit. A variety of approaches have been proposed. In this sense, some approaches propose a visual representation of the routine, while others only provide a time series of binary flag values, and do not propose any specific visualization of the flagged data as opposed to non-flagged ones. In this regard, the present paper puts forward a complete routine including several quality control procedures for solar irradiance measurements by providing visual support for these different approaches. The visual tool in question was validated using five years research data with 10 minutes resolution of the global, diffuse and direct components of solar irradiation collected from three ground-based weather stations in Morocco. This visual tool puts forth a more precise idea of the measurement quality by detecting various errors, such as time shifts, outliers identification; either with one or two components, or consistency tests between the three components of solar radiation when available. The proposed tool can be regarded as a means of improving the detection rate of abnormal data as a first step in diagnosing the prominent causes of error.

Wearable sensing techniques to understand pedestrian-level outdoor microclimate affecting heat related risk in urban parks

Around the world people are rapidly moving to cities. The rapid urbanization has led to changes in land use and land cover, which (i) modify the urban surface energy balance making the cities hotter than the surrounding rural area, (ii) exacerbate the impact of extreme weather events like heatwaves and (iii) lead to poor quality of life. City administrations around the world are undertaking drastic measures to mitigate extreme heat. Here, in this study we test a wearable sensing platform to both study pedestrian-level microclimate boundary conditions as well as the impact of urban greening on moderating excess heat in dense urban areas. The experiment presented here was conducted in New York City. Typically, the urban thermal state is estimated by means of direct observations from ground-based sensors, satellite based remote sensing techniques, and high-resolution urban climate modeling, all of which are too coarse to resolve pedestrian-level impacts, a key parameter in determining heat stress for citizens occupying the outdoors. Here a wearable and a portable sensing apparata were used to monitor key environmental parameters - air temperature, relative humidity, wind speed, mean radiant temperature and solar radiation. The coupled monitoring platform included multiple sensors and a wireless data logging system which were all prototyped by the authors on open source technologies. The sensor platform was able to accurately map the thermal environment of multiple dense urban spaces, being to reproduce the spatial variability in key microclimate parameters, and the performance was comparable to traditional stationary ground-based weather stations. The results, which may be of key help also for validating microclimate forecasting models, indicate high spatial variability in temperature, humidity and solar radiation within the same urban parks. Our findings also indicate that on average small urban parks in a dense urban setting were able to reduce the air temperature by 3–7 °C in New York City with major gains during the mid-afternoon periods.

Distributed placement of wind farms to minimize the impact of power fluctuations on the electric power system

The problem of the influence of power fluctuations of wind farms due to the variability of the wind speed on the electric power system is considered. With high wind energy penetration, an increase in the operating reserve in electric power systems is required to cover possible sudden power fluctuations. One of the ways to reduce the stochastic nature of the wind farms power generation is their geographically distributed location. A method is proposed for the selection of capacity and distributed placement of wind farms, taking into account the factor of the variability of the total generated power. In each of the prospective areas for wind farm placement, the simulation of electricity generation by a single wind turbine with hour-by-hour breakdown is carried out using the developed WindMCA software based on long-term ground-based weather stations data. Optimization of wind farms capacity and their distributed placement in areas is carried out using a genetic algorithm in the MATLAB environment. The target function is the coefficient of variation of the power generated by all wind farms in the areas under consideration, depending on the number of wind turbines therein. Power duration curves are used in the final comparison of wind farms siting options. The application of the method is carried out on the example of the wind farms placement in the Zabaykalsky Krai. A solution has been obtained that provides a minimum coefficient of variation of the wind farms generated power and a relatively high capacity utilization factor. With a distributed location of wind farms, the duration of the period with the maximum output is reduced, however, the duration of low power generation is significantly increased. With an increase in the number of wind farms connected to various nodes of the electric power system, a certain guaranteed level of power generation can be obtained, which, ultimately, will reduce the required amount of the reserve of generating capacities.

Evaluation of the Climate Forecast System Reanalysis data for hydrological model in the Arctic watershed Målselv

Abstract The high-resolution Climate Forecast System Reanalysis (CFSR) data have recently become an alternative input for hydrological models in data-sparse regions. However, the quality of CFSR data for running hydrological models in the Arctic is not well studied yet. This paper aims to compare the quality of CFSR data with ground-based data for hydrological modeling in an Arctic watershed, Målselv. The QSWAT model, a coupling of the hydrological model SWAT (soil and water assessment tool) and the QGIS, was applied in this study. The model ran from 1995 to 2012 with a 3-year warm-up period (1995–1997). Calibration (1998–2007), validation (2008–2012), and uncertainty analyses were performed by the Sequential Uncertainty Fitting Version 2 (SUFI-2) algorithm in the SWAT Calibration Uncertainties Program for each dataset at five hydro-gauging stations within the watershed. The objective function Nash–Sutcliffe coefficient of efficiency for calibration is 0.65–0.82 with CFSR data and 0.55–0.74 with ground-based data, which indicate higher performance of the high-resolution CFSR data than the existing scattered ground-based data. The CFSR weather grid points showed higher variation in precipitation than the ground-based weather stations across the whole watershed. The calculated average annual rainfall by CFSR data for the whole watershed is approximately 24% higher than that by ground-based data, which results in some higher water balance components. The CFSR data also demonstrates its high capacities to replicate the streamflow hydrograph, in terms of timing and magnitude of peak and low flow. Through examination of the uncertainty coefficients P-factors (≥0.7) and R-factors (≤1.5), this study concludes that CFSR data is a reliable source for running hydrological models in the Arctic watershed Målselv.

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.