Daily Satellite Data Research Articles

PPP Solution‐Based Model of Absolute Vertical Movements of the Earth's Crust in Poland With Consideration of Geological, Tectonic, Hydrological and Mineral Information

AbstractThis study aims to develop an absolute model of contemporary Vertical Crustal Movements (VCM) and Vertical Land Movements (VLM) in an area of Poland based on GNSS solutions. Velocities at permanent stations were subjected to geological, tectonic, hydrological and mineral information analyses. Reliability analysis and estimation of velocities at individual GNSS stations, comparative analysis of results and spatial analysis were carried out. Vertical velocities were determined using four computational strategies. Daily satellite data in the ITRF2014 system collected from permanent GNSS stations of the Polish part of the ASG EUPOS system were obtained from the Polish Main Office of Surveying and Cartography. All the data were from the 2011 to 2021 time period (approx. 11 years) and obtained in Rinex format. Time series from the Precise Point Positioning (PPP) solution calculated using GipsyX software were used. The absolute vertical crustal velocities obtained for Poland mostly vary between +1.0 and −1.0 mm/yr, which is 95% of the values obtained within local extremes. This region of Poland can be considered tectonically stable and the developed VCM model correlates with the geological and tectonic structure of the region. Taking into account the influence of tectonics, geology, hydrology and location of mineral resources has allowed better interpretation of vertical velocities and correction of the associated model. The proposed computational strategy based on combining data sets developed by different methods gave good results.

Increasing marine heatwaves in the Gulf of Thailand after the global warming hiatus

Marine heatwaves (MHWs) have been reported often throughout the world, producing severe effects on marine ecosystems. However, the spatial pattern and trend of MHWs in the Gulf of Thailand (GOT) is still unknown. Based on high-resolution daily satellite data over a 40-year period from 1982 to 2021, changes in annual mean SST and MHW occurrences across the GOT are explored here. The results demonstrate that during a warming hiatus (1998–2009), annual mean SST in the GOT encountered a dropping trend, followed by an increasing trend during a warming reacceleration period (2010–2021). Although a warming hiatus and a warming reacceleration occurred in the annual mean SST after 1998, regional averaged SSTs were still 0.18 °C–0.42 °C higher than that for 1982–1997. Statistical distributions reveal that there was a significant shift in both annual mean SSTs and annual extreme hot SSTs. These changes have the potential to increase the frequency of MHWs. Further analysis reveals that MHW frequency has increased at a rate of 1.11 events per decade from 1982 to 2021, which is 2.5 times the global mean rate. For the period 2010–2021, the frequency and intensity of MHWs in the GOT have never dropped, but have instead been more frequent, longer lasting and extreme than those metrics of MHWs between 1982 and 2009. Furthermore, the findings highlight significant changes in the SST over the GOT that may lead us to change or modify the reference period of the MHW definition. The findings also suggest that heat transport and redistribution mechanisms in the GOT sea are changing. This study contributes to our understanding of MHW features in the GOT and the implications for marine ecosystems.

An Optimized Variational Processing Method Based on Satellite-Station Data on Snow Cover Days on the Qinghai–Tibet Plateau

The Qinghai–Tibet Plateau is a sensitive area to climate change, and snow cover has an important impact. Due to the high altitude and complex terrain, station observations of snow cover on the plateau are sparse but objective, while satellite data have high resolution but limited accuracy. Therefore, an optimized variational processing method based on daily satellite data from 1989 to 2020 and monthly snow cover day data from stations is used to combine their advantages, and a high-resolution (0.1° × 0.1°) monthly dataset of snow cover days during 1989–2020 is obtained. This study analyzes the spatial and temporal characteristics of snow cover days on the Qinghai–Tibet Plateau over the past 30 years and compares the differences before and after applying the optimized variational processing method. The variational processing method is also used to reanalyze data on temperature and precipitation. This study confirms the objectivity of the processing method and reveals the regional characteristics of snow cover days and their correlation with temperature and precipitation. The data obtained after optimized variational processing provide a more accurate and detailed representation of the spatial and temporal characteristics of snow cover days. The distribution and variation trends of snow cover days on the Qinghai–Tibet Plateau exhibit significant spatial differences. The average number of snow cover days during the snow season is 45.51 d, with 22.74 d in winter. The Qaidam Basin and the southwestern part of the plateau are areas with low snow cover days, while high-altitude mountainous areas have higher values. Overall, there is no significant change in snow cover days during the snow season, but there is a significant decreasing trend of −1.50 d/10 yr in winter. The snow cover days in the plateau’s hinterland and low-altitude areas mainly show a decreasing trend, while high-altitude mountainous areas show an increasing trend. Snow cover days in the western part of the Qinghai–Tibet Plateau are both influenced by temperature and precipitation in winter, while precipitation dominates in the eastern part.

Hydrography and deep chlorophyll maximum patterns of the Athos Basin and the Thracian Sea continental shelf (North Aegean Sea) combining glider and satellite observations

This work focuses on the North Aegean Sea deep chlorophyll maximum (DCM) dynamics, as revealed during the first glider mission over the continental shelf and slope of the Thracian Sea and along the Athos basin, and presents a conceptual model on the response of the DCM in relation to the prevailing stratification/mixing of the water column. Water temperature, conductivity, salinity, chlorophyll-a, CDOM, DO and particle backscatter data (bb700), were recorded during this underwater glider mission in summer 2019. Daily satellite data from Sentinel 3 complemented the glider collected data to validate the satellite-derived estimates of surface Chlorophyll-a levels. The high-resolution physical and chemical observations were analyzed emphasizing on thermal stratification, pycnocline strength, CDOM and dissolved oxygen vertical distribution and the presence of deep chlorophyll maximum (DCM). DCM statistical characteristics were derived by fitting a Gaussian model on the chlorophyll-a profiles. The mean DCM was observed at 76 ± 14.5 m depth, located below the euphotic layer, estimated at 64 ± 2.1 m, with the water column dynamics affecting directly its depth, thickness and peak prominence. The satellite-derived chlorophyll-a, integrated within the euphotic layer, was found in fair agreement to the integrated chlorophyll-a values from surface to optical depth recorded by the glider. Chl-a at the DCM were found up to 0.60 mg m−3, the vertically-integrated pigment content was estimated at 45 ± 14 mg m−2 and the values of Optical Community Index (OCI) illustrate the dominance of small-sized plankton. The increased variability in the particulate backscattering coefficient bb700 in Thracian Sea shelf and slope deep waters seems related to sediment resuspension. CDOM levels increased from 0.76 ppb at the sea surface to 1.11 ppb at 40 m depth, being positively correlated to Chl-a within the DCM. CDOM concentration increased steadily at intermediate and deeper waters up to 1.34 ± 0.07 ppb. DO profiles revealed the presence of an oxycline in the DCM layer, capable to oxygenate the intermediate and deeper waters (+3 × 10−3 ml/L/m). Bottom DO in Athos basin was measured at 5.0 ml L−1, indicating that the apparent oxygen utilization in Athos Basin is approximately 50 μmol L−1.

Is climate really changing? Insights from analysis of 30-year daily CHIRPS and station rainfall data in Zimbabwe

Climate change debate has been ongoing for at least two decades world over. However, an understanding of how much change has occurred in developing countries remains limited. Using a combination of daily Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and gauge rainfall data, this study analysed whether rainfall characteristics over 30 years (1989 to 2018) in Zimbabwe exhibited any significant trends. Firstly, Kappa statistic and a confusion matrix were used to test the appropriateness of CHIRPS satellite derived rainfall estimates for carrying out trend analysis. Thereafter, the Mann Kendall test, Sen's slope and kriging were used, to detect trends, ascertain magnitude of change and map the spatial variation in rainfall characteristics, respectively. The rainfall characteristics included: Total Number of Wet Days (TNWD), Maximum Number of Consecutive Wet Days (MNCWD), Maximum Number of Consecutive Dry Days (MNCDD) and Maximum Rainfall per Day (MRD). Validation results showed that overall accuracy ranged from 0.862 to 0.897 while kappa statistic ranged from 0.739 to 0.81, suggesting a substantial agreement between satellite derived- and gauge rainfall. No statistically significant trends were detected for the four rainfall characteristics analysed at 95% confidence interval. Although no significant trends were detected at 95% confidence interval to signal climate change it was observed that MNCDD, MRD and TNWD were increasing while MNCWD was generally decreasing. This key finding opens new opportunities for geographically targeted and integrated intervention strategies for agricultural and water management in Zimbabwe. This is particularly important considering that Zimbabwe mainly depends on rain fed agriculture and understanding of the key rainfall characteristics can enable proper planning and decision making in terms of adaptation that which can ensure water availability and food security. Food security is fundamental in achieving national security. In addition, this study opens opportunities for further studies on the factors that influence the trends through using sub-daily and daily satellite data for a relatively longer period i.e., >30 years. It is therefore our contention that fine spatial and temporal scales adopted in this study yield better characterisation of changes in rainfall regimes, hence brings out better insights on understanding intra-seasonal fluctuations for climate change detection studies.

Summer marine heatwaves in the South China Sea: Trend, variability and possible causes

Marine heatwaves (MHWs) have been frequently observed worldwide, causing devastating impacts on marine organisms and ecosystems, but the trend of MHWs is still unclear in the South China Sea (SCS). Here, the long-term trend and inter-annual variability of the summer SCS MHW events are investigated based on the high-resolution daily satellite data. The results revealed remarkable increases in the duration, intensity, coverage, and severity during 1982–2019, indicating that the SCS MHW events have become more frequent, intense, extensive, and serious. The probability ratio of SCS MHW events is four times during the 2010s of that during the 1980s. The increasing trend can be largely attributed to the long-term increase in the mean SCS temperature. The inter-annual variability of the SCS MHWs is linked closely to the El Niño and Southern Oscillation, with more/less MHW events occurring during the following summer after the El Niño/La Niña events. A diagnosis of synoptic-scale heat budget suggests that the extreme SCS warming can be explained by the combined effects of positive surface heat flux largely due to the enhanced shortwave radiation and convergence of oceanic advection in association with an anomalous upper-ocean anticyclone. The effect of surface heat flux seems to be predominant over the large spatial coverage, whereas oceanic heat transport is also important in some specific regions. The large-scale anticyclonic circulation anomalies over the northwestern Pacific accompanying the westward-extending western Pacific subtropical high during the El Niño decay summers play an essential role in the building-up and persistence of the extreme warming, which has important implications for the prediction of the SCS MHWs.

Wildfire, smoke, and outdoor recreation in the western United States

Wildfire activity is increasing in the western United States at a time when outdoor recreation is growing in popularity. Because peak outdoor recreation and wildfire seasons overlap, fires can disrupt recreation and expose people to poor air quality. We link daily data on campground use at 1069 public campgrounds across the western United States over a ten-year period to daily satellite data on wildfire and smoke. We use this data set to (1) tabulate the number of campers affected by wildfire and smoke at campgrounds across the western US, and (2) provide estimates of how campground use responds to wildfire and smoke impacts, including the first causal estimates of the impacts of wildfire smoke on recreation behavior. We find that, on average, more than 120,000 campground visitor-days per year are close to an actively burning fire and nearly 400,000 are impacted by adverse smoke conditions. In some regions more than 10% of camper-days occur when air quality is poor due to wildfire smoke. Combining the results with monthly national park visitation data at the 30 parks in our sample, we estimate that fire and smoke affect 400,000 and 1 million visitor-days per year, respectively. Using fixed effects panel regressions at the campground level, we estimate declines in campground use in response to fire and smoke. The magnitude of the smoke effect is small, however, suggesting that smoke fails to deter most visitors to public lands. Back-of-the envelope calculations based on our findings and estimates from the literature suggest that most of the total welfare losses accruing to campers due to smoke occurs via health impacts from trips taken in spite of smoky conditions, rather than due to cancelled trips.

Lockdowns Save People from Air Pollution: Evidence from Daily Global Tropospheric NO2 Satellite Data

Motivated by the global fear of the Coronavirus-19 (COVID-19) pandemic, we investigated whether lockdowns save people from air pollution, notably from Nitrogen Dioxide (NO2). Using daily satellite data from the National Aeronautics and Space Administration (NASA), we first found that the global NO2 tropospheric vertical column density (TVCD) decreased by 16.5% after the Coronavirus-19 (COVID-19) outbreak. Then, we calculated the global health benefits, as the monetized value of life, using the value of a statistical life (VSL). The total global health benefits were approximately 8.73 trillion USD, accounting for 10% of the global GDP; such benefits would be the largest in China, followed by the United States, Japan and Germany. Our results suggest that lockdowns may bring benefits to countries that policy interventions cannot easily bring, thus highlighting the importance of social distancing.

Evaluation of fractional clear sky over potential astronomical sites

ABSTRACT The estimation of the night-time cloud fraction (CF) is found to be one of the key parameters for evaluating the number of useful nights at an astronomical site. This work evaluates useful astronomical night-time observation over eight sites using a minimum threshold of CF from 21 years (2000–2020) of ground-based hourly visual and daily satellite data along with 41 years (1980–2020) of long-term hourly reanalysis data. The estimated number of photometric nights is underestimated by 8–24 per cent using the reanalysis data at Indian Astronomical Observatory-Hanle in comparison with the visual observations, while the estimated number of spectroscopic nights is 70–75 per cent per year and in good agreement with the visual observations. Among the astronomical sites, Paranal is found to be the best for astronomical observations, with 87 per cent spectroscopic nights per year. On the other hand, Hanle, Ali and Devasthal, located in the Himalayan region, exhibit an average of 68–78 per cent spectroscopic nights per year based on long-term reanalysis data, while Merak exhibits 61–68 per cent spectroscopic nights per year. Vertical profiles and global horizontal distributions for CF and related variable parameters are further compared among the sites. Global CF trends based on 41 years of reanalysis data show a decreasing tendency over most land regions and an increasing tendency over oceanic regions as well as over the Sahara desert, Middle East, and Indian subcontinent along the adjacent Tibetan Plateau. Such different CF trends between the ocean and land regions are thought to be the result of differential surface warming and water vapour changes associated with climate change.

Assessment of the Relationship between Cerebrospinal Meningitis and Climate Variables in Kaduna State, Nigeria

This study examines the relationship between Meningitis and weather parameters (air temperature, maximum temperature, relative humidity, and rainfall) in Kaduna state, Nigeria on a weekly basis from 2007–2019. Meningitis data was acquired weekly from Nigeria Centre for Disease Control (NCDC), Bureau of Statistics and weather parameters were sourced from daily satellite data set National Oceanic and Atmospheric Administration (NOAA), International Research Institute for Climate and Society (IRI). The daily data were aggregated weekly to suit the study. The data were analysed using linear trend and Pearson correlation for relationship. The linear trend results revealed a weekly decline in Cerebro Spinal Meningitis (CSM), wind speed, maximum and air temperature and an increase in relative humidity and rainfall. Generally, results reveal that the most important explanatory weather variables influencing CSM amongst the five (5) are the weekly maximum temperature and air temperature with a positive correlation of 0.768 and 0.773. This study recommends that keen interest be placed on temperature as they play an essential role in the transmission of this disease and most times aggravate the patients' condition.

Estimation of Hourly Clearness Index and Diffuse Fraction Over Coastal and Sahel Regions of Nigeria Using NCEP/NCAR Satellite Data

In this paper, daily satellite data of global and diffuse irradiances obtained from the archives of National Centre for Environmental Prediction (NCEP) and National Centre for Atmospheric Research (NCAR) covering a period of ten years (2005-2014) have been employed to study clearness index (KT) and diffuse fraction (KD) distributions over six carefully selected meteorological stations in Nigeria. These stations are Port Harcourt, Akure and Enugu in the coastal region; Kano, Maiduguri and Bauchi in the sahelian region. Results have shown that while global irradiance show double peaks in its mean annual daily variation at both regions, diffuse radiation only show this in the sahelian region. In the coastal region, its values are almost uniform throughout the year .In observing the synoptic hourly distribution of clearness index and diffuse fraction, it was found that while clearness index exhibits minima values at hours close to sunrise and sunset, the reverse is the case for diffuse fraction. Also that in the coastal region, clear sky condition is prevalent in Akure and Enugu at both seasons whereas in Port Harcourt, it seldomly occurs. In the sahelian region, on the other hand, clear sky condition is prevalent at both seasons. Using ANOVA method, empirical models were developed for KT. All the models at the stations and regions gave good and significant coefficient of determination R2. During the dry season months, it ranges between 0.98 and 0.99 while during the rainy season, it is 0.89 and 0.95. To further test the efficiencies of the developed models, the Mean Bias Error (MBE) and Root Mean Square Error (RMSE) were computed. Values obtained for both MBE and RMSE for most of the stations are low and positive. These are desirable results for good and significant models. It shows that these models can be used to predict clearness index KTwith high accuracy in the selected stations and proximateones.

Offshore Wind Energy Resource in the Kingdom of Morocco: Assessment of the Seasonal Potential Variability Based on Satellite Data

This study provides a first estimate of the offshore wind power potential along the Moroccan Atlantic shelf based on remotely sensed data. An in-depth knowledge of wind potential characteristics allows assessment of the offshore wind energy project. Based on consistent daily satellite data retrieved from the Advanced Scatterometer (ASCAT) spanning the period from 2008 to 2017, the seasonal wind characteristics were statistically analyzed using the climatological Weibull distribution functions and an assessment of the Moroccan potential coastal wind energy resources was qualitatively analyzed across a range of sites likely to be suitable for possible exploitation. Also, an atlas of wind power density (WPD) at a height of 80 m was provided for the whole Moroccan coast. An examination of the bathymetrical conditions of the study area was carried out since bathymetry is among the primary factors that need to be examined with the wind potential during offshore wind project planning. The results were presented based on the average wind intensity and the prevailing direction, and also the wind power density was shown at monthly, seasonal and interannual time scale. The analysis indicated that the coastal wind regime of the southern area of Morocco has the greatest energy potential, with an average power density which can reach in some places a value around 450 W/m2 at heights of 10 m and 80 m above sea level (a.s.l) (wind turbine hub height) more particularly in the south of the country.

Quantitative modeling of cyanobacterial concentration using MODIS imagery in the Southern Caspian Sea

The cyanobacterial harmful algal blooms have been observed, over the last decade, in several regions of the southern Caspian Sea, becoming a major threat to human health and aquatic life. The present study aims to develop two models to quantify cyanobacterial concentration in the Caspian Sea using artificial neural networks and multiple band linear regression. The models are based on Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. Data were collected from the west, center, and east of the southern Caspian Sea between September 2015 and August 2016. The field dataset includes 123 samples in seven different transects and is used to define and evaluate the proposed methods. The Root Mean Square Error (RMSE), unbiased RMSE (URMSE), and correlation coefficient (R) values between Multiple Band Linear Regression Algorithm outputs and field dataset are 1.8 × 10−3 mg.m−3, 22.43%, and 0.73, respectively. For Artificial Neural Network (ANN), the outputs are 1.6 × 10−3 mg.m−3, 18.89%, and 0.81, respectively. The performance of the proposed methods is proven suitable under nearly all conditions of the southern Caspian Sea. However, numerical comparison and visual evaluation of the results show that the ANN method is less sensitive to small changes in the environmental conditions, leading to more stable results. Moreover, the ANN model provides accurate results in most cases, and the accuracy of this results are improved by increasing the training data. This study focused on the development and validation of an optimal algorithm for quantifying temporal and spatial variability phycocyanin concentrations in the Caspian Sea using daily satellite data.

The Study of the State of the World Ocean Areas Based on Modern Technologies for Processing and Analysing Data

This paper is devoted to the processing of hydrophysical data on the state of marine areas utilizing modern data processing technologies. Author uses Python programming language to process marine data. The results on the processing of the remote sensing data on the Baltic and the Black Seas surface temperature are presented in the study. Daily satellite data for the years 1982–2018 were processed using Python scripts for both water areas. The analysis of the obtained information was conducted with R programming language. This language supports a wide range of statistical and numerical methods and is convenient to use to determine the statistical characteristics of the studied quantities. Data analysis reveals statistically significant positive trend in the sea surface temperature (SST) values in the both studied water areas over the past decades. The paper has also analyzed the spatial variability of the surface temperature within considered areas. The analysis illustrates the temperature variability in certain areas of the sea and identifies areas with the highest rate of temperature increase. Conclusions are made on the usability of the programming languages for various data processing tasks.

Effect of Caribbean Water incursion into the Gulf of Mexico derived from absolute dynamic topography, satellite data, and remotely sensed chlorophyll <i>a</i>

Abstract. The dynamics of the Loop Current (LC) and the detached Loop Current eddies (LCEs) dominate the surface circulation of the Gulf of Mexico (GoM) and transport Caribbean Water (CW) into the gulf. In this work, 25 years (1993–2017) of daily satellite data are used to investigate the variability of these physical processes and their effect on chlorophyll a (Chl a) concentrations from 1998 to 2017, including temporal changes, mean differences, and regional concentration tendencies. The physical variables analyzed are absolute dynamic topography (ADT) and oceanic currents. From the ADT and oceanic current monthly climatologies, it is shown that there is an annual intrusion of CW with an inward incursion that starts in spring, peaks in the summer, reaches to 28∘ N and 90.45∘ W, and then retreats in winter to approximately 26.5 ∘ N and 88.3 ∘ W. Minimum surface Chl a concentrations (< 0.08 mg m−3) are found during the summer–autumn period inside the region of maximum incursion of CW; the opposite is observed during the winter period when Chl a concentrations were at a maximum, e.g., > 0.14 mg m−3. The 3-year running averages of the ADT 40 cm isoline qualitatively reproduce the climatological pattern of 25 years showing that before 2002 CW was less intrusive. This suggests that from 2003 onward, larger volumes of oligotrophic waters from the Caribbean Sea have invaded the western GoM and reduced mean surface Chl a concentrations. A direct comparison between the 1998–2002 and 2009–2014 periods indicates that in the latter time interval, the Chl a concentration above waters deeper than 250 m has decreased significantly.

Evaluation of surface shortwave and longwave downwelling radiations over the global tropical oceans

In the present study, daily downwelling shortwave (QS) and longwave radiation (QL) data from one satellite and two hybrid products have been evaluated using Global Tropical Moored Buoy Array during 2001–2009 in the tropical oceans. Daily satellite data are used from the Clouds and Earth’s Radiant Energy System (CERES) program. Data are obtained using Moderate Resolution Imaging Spectroradiometer (MODIS) (CM) aboard the Terra and Aqua satellites. Coordinated Ocean Research Experiments (CORE-II) and Tropical Flux data (TropFlux) are the other two hybrid products used in this study. The analysis shows that majority of QS observations as well as derived products lie in 200–300 Wm−2 range in all the three tropical oceans. Both QS and QL in all products overestimated the majority of the observations. Yet, they underestimated the lower (0–100 Wm−2) values in QS and higher (300–440 Wm−2) values in QL. Majority of the QL observations lie within 390–420 Wm−2 range, and CM slightly overestimated this observed distribution in the Pacific and the Atlantic Oceans. But, majority of the observations in the Indian Ocean lie within 420–450 Wm−2 range. This implies that the tropical Indian Ocean receives 30 Wm−2 more energy as compared to the tropical Pacific and the Atlantic in the form of downwelling longwave radiation. Daily observed QS shows dominant seasonal cycle over the central, the eastern Pacific and the eastern Atlantic. On the other hand, the western Pacific, the central Atlantic and the Indian Oceans show intraseasonal variations. All products show this variation with high root-mean-square error (RMSE) values (QS and QL) over the Indian Ocean than in the Pacific and the Atlantic Oceans. Downwelling radiation from CORE-II shows highest RMSE (for both QS and QL) with least correlation coefficient (CC), and TropFlux has lowest RMSE and highest CC among all products in all three tropical oceans. CM has intermediate values of standard deviation, CC and RMSE. These results are not seasonally dependent, since the seasonal statistics are consistent with seasonal changes. Assuming that the SST is only driven by the downwelling shortwave and longwave fluxes, the errors associated with monthly SST can be as large as 0.2–0.3 (0.1–0.2) °C associated with errors in QS (QL). Both QS and QL in CORE-II have lower spatial variability as compared to other datasets. QL in the tropical oceans shows seasonal spatial variability determined by intertropical convergence zone positions. This variability does not change significantly over the Pacific and the Atlantic Oceans. The summer and winter monsoon patterns in the Indian Ocean guide the QL variability. Opposite to QS, higher QL values have lower variability. Thus, this study aims at finding better radiation dataset to use in the numerical models and deduce that satellite data could be an alternative to existing reanalysis products.

High‐resolution mapping of daily climate variables by aggregating multiple spatial data sets with the random forest algorithm over the conterminous United States

High‐resolution gridded climate data products are crucial to research and practical applications in climatology, hydrology, ecology, agriculture, and public health. Previous works to produce multiple data sets were limited by the availability of input data as well as computational techniques. With advances in machine learning and the availability of several daily satellite data sets providing unprecedented information at 1 km or higher spatial resolutions, it is now possible to improve upon earlier data sets in terms of representing spatial variability. We developed the NEX (NASA Earth Exchange) Gridded Daily Meteorology (NEX‐GDM) model, which can estimate the spatial pattern of regional surface climate variables by aggregating several dozen two‐dimensional data sets and ground weather station data. NEX‐GDM does not require physical assumptions and can easily extend spatially and temporally. NEX‐GDM employs the random forest algorithm for estimation, which allows us to find the best estimate from the spatially continuous data sets. We used the NEX‐GDM model to produce historical 1‐km daily spatial data for the conterminous United States from 1979 to 2017, including precipitation, minimum temperature, maximum temperature, dew point temperature, wind speed, and solar radiation. In this study, NEX‐GDM ingested a total of 30 spatial variables from 13 different data sets, including satellite, reanalysis, radar, and topography data. Generally, the spatial patterns of precipitation and temperature produced were similar to previous data sets with the exception of mountain regions in the western United States. The analyses for each spatially continuous data set show that satellite and reanalysis led to better estimates and that the incorporation of satellite data allowed NEX‐GDM to capture the spatial patterns associated with urban heat island effects. The NEX‐GDM data is available to the community through the NEX data portal.

Interannual and Intra-Monthly Fluctuations of the Wind Field and Sea Surface Temperature in the West African Upwelling Region Based on Satellite Data

The relative intensity of fluctuations of the sea surface temperature and wind field in the West African upwelling region within a month (intra-monthly) and from year to year were studied using highly resolved daily satellite data from the 1980s until 2015. It is shown that the interannual and intra-monthly variances are similar or the latter prevail throughout a year (for the wind field) and most of a year (for the sea surface temperature). This should be taken into account during the analysis of the upwelling characteristics (especially in the case of data with low spatio-temporal resolution) to prevent aliasing.

Day by day evolution of a vigorous two wave Saharan dust storm – Thermal and air quality impacts

This paper conducts a day-by-day analysis of an intense Saharan dust storm that struck Athens (Greece) during April 2008 and consisted of two main dust waves (the first from 10/04/2008 to 14/04/2008 and the second from 19/04/2008 to 22/04/2008). Daily satellite data (1o × 1o resolution) of aerosol optical depth (AOD) were used, in order to follow the course of two dust plumes originated in North Africa through the eastern Mediterranean. Ground based measurements of particle concentrations (PM 10 and PM 2.5 ) and meteorological parameters (wind speed, air temperature, relative humidity and visibility) were also studied. The time intervals before and after each dust wave were taken under consideration, aiming to identify the atmospheric circulation that generated the dust storm and also the clearing mechanisms that removed the African dust from the Athenian atmosphere. Backward air mass trajectories isolated the source of the two dust plumes over Libya and Tunisia, where extreme daily AOD values were recorded. The transportation of dust in the Greek region, during both dust waves, was associated with south westerlies airflows attributed to the advent of low pressure systems from the Atlantic. The impact of both dust waves in Athens was interrupted by the prevalence of a strong north westerlies air stream, which carried the particles eastwards through Turkey, Cyprus and the Middle East. During this dust event, air quality in Athens was severely downgraded, but no thermal discomfort has occurred, according to the estimation of a Pollution Index (PI) and a Discomfort Index (DI), respectively. A drop in visibility was also reported.

A multi-step scheme for spatial analysis of solar and photovoltaic production variability and complementarity

Renewable energy resources are variable by nature. Due to this fact conventional electricity systems, which were designed for centralized generation, have to follow a different management approach when a big share of these technologies take part into the system. The space-time variability characteristics of solar radiation, wind and precipitation are very different and a detailed understanding of them is important for an adequate planning and management of the electricity system.This paper is focused on solar irradiation as source of energy for photovoltaic (PV) generation, but the proposed scheme can generally be applied to other renewable resources and different solar irradiation applications. A comprehensive methodology to analyze variability and complementarity of solar resource and PV production among sub-regions of a wide area is developed. The photovoltaic energy yield is defined as kWh per kW installed, which facilitates the comparison among sub-regions and allows a comparison with other renewable energies like solar thermoelectric or wind energy. The multi-step approach facilitates the spatial evaluation and comparison among areas and it could be applied to different time resolutions, from a short term analysis to climatological perspective as well as a climate change projections analysis.The main steps of the method are the application of an objective clustering method for performing a regionalization of the whole domain, the analysis of the temporal variability of solar radiation and photovoltaic energy yield, and the intercomparison of the obtained clusters for examining their complementarity.As an implementation example, we make use of 30years of daily satellite data, which is usually considered as the time for defining a climatology, and a long-term overview of solar variability is analyzed over the Iberian Peninsula (IP). In this region, the internal interconnections of the electrical system are strong, but the external interconnections are rather limited. The spatial distribution and variability of photovoltaic (PV) power yield is calculated for different tracking systems. The variability is analyzed on an interannual time scale, which is relevant for energy supply security and year-to-year price stability. The interannual variability shows robustness and stability of solar radiation and PV production on average for the whole domain, but with significant differences among clusters that could allow spatial compensation of PV production. The relationship between the variability of solar irradiation and of PV yield is not uniform among the different clusters. Areas where solar irradiation is higher are more sensitive to tracking type.The whole process described in the article provides the information of how solar resource and the PV energy yield perform in a limited area and provide the tools to analyze the relationships between sub-areas and their variability. In this sense, this method can be applied for isolated or nearly isolated electric systems located in regions with a variety of climates, or for interconnected systems involving several countries.