Ground-based Meteorological Data Research Articles

A multiclass short-term lightning predictor for the Amazon region using ground-based meteorological data and artificial neural network

Lightning prediction is a complex task due to the intricate nature of this phenomenon. Various sectors of society, including aviation and civil defense, can benefit from accurate lightning occurrence information. Methodologies that can predict lightning occurrence are crucial to minimize the issues related to this phenomenon. In this work, we propose a novel approach using the machine learning model called Artificial Neural Network (ANN) to predict lightning occurrence one hour ahead. We use ground-based meteorological data from the National Institute of Meteorology (INMET) and the number of lightning per hour to feed the ANN model. We used a lightning dataset of six years of observation (2015–2020) from GLD360 to train, validate, and test machine learning algorithm models. A model was developed for each weather station. We use data from 24 stations distributed throughout the Brazilian Legal Amazon region. The multiclass predictor was based on three categories: no lightning, low-intensity, and high-intensity of lightning. We also developed a solution comprising two cascade ANN models, each performing binary prediction. The first predicts the occurrence or no occurrence of lightning, and the second predicts the intensity: low or high. The cascade approach showed superior performance compared to the regular approach.

QGIS AND OPEN DATA CUBE APPLICATIONS FOR LOCAL CLIMATE ZONES ANALYSIS LEVERAGING PRISMA HYPERSPECTRAL SATELLITE DATA

Abstract. Climate change poses a significant threat to humans and biodiversity, impacting various aspects of livelihoods, infrastructure, and ecosystems. Understanding climate change and its interaction with the environment is crucial for achieving Sustainable Development Goals. Local Climate Zones (LCZ) play a key role in comprehending climate change by categorizing urban areas also based on their thermal characteristics. This study presents prototype open-source software tools developed to integrate ground and satellite data for LCZ analysis in the Metropolitan City of Milan (Northern Italy). These tools consist of a QGIS plugin to access and preprocess ground-based meteorological sensor data and a client-server platform, based on the Open Data Cube and Docker technologies, for the exploitation of multispectral and hyperspectral satellite data in LCZ mapping and analysis. The tools’ architecture, data retrieval methods, and analysis capabilities are described in detail. The QGIS plugin facilitates the access and preprocessing of ground-based sensor data within the user-friendly QGIS environment. The platform enables seamless ground-sensor and satellite data management and analysis, using Jupyter Notebooks as an interface to support programmatic operations on the data. The proposed tools provide a framework for studying climate change and its local impacts on urban environments, with the potential of empowering users to effectively analyze and mitigate its effects.

DSRSS-Net: Improved-Resolution Snow Cover Mapping from FY-4A Satellite Images Using the Dual-Branch Super-Resolution Semantic Segmentation Network

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.

Effect of aggregation and disaggregation of land surface temperature imagery on evapotranspiration estimation

Understanding the spatial distribution of evapotranspiration (ET) and other surface energy fluxes in tree crops is vital for optimizing precision irrigation management practices. The land surface temperature (LST) is the primary input to estimate ET at field scale, but currently, available satellite data have a coarse resolution, which is not suitable for estimating ET at the required spatiotemporal scale at the field level. The development of unmanned aerial vehicles (UAVs) or airborne platforms during the last two decades has enabled the acquisition of LST and subsequent derivation of ET at the finer spatial resolution of up to a few centimeters due to the advances in low-weight thermal cameras onboard these platforms. In this study, the disaggregation methodology was applied to Landsat imagery based on aerial imagery collected at 0.5 m resolution over almond orchards in the Sacramento Valley of California, USA, to estimate ET at the orchard scale. The TsHARP algorithm was applied to sharpen the Landsat LST images (30 m spatial resolution) to target resolutions of 0.5, 10, 20 m. Also, the simple average aggregation technique was used to aggregate the aerial imagery (0.5 resolution) to 10, 20, and 30 m scales. Two Source Energy Balance (TSEB) model was used to estimate spatially distributed ET fluxes by combining the ground-based meteorological data for aggregated and disaggregated LST images. The estimated ET fluxes were compared to the measured eddy covariance fluxes, and then the aggregation and disaggregation processes were evaluated at 0.5, 10, 20, and 30 m spatial scales. At four spatial scales (0.5, 10, 20, and 30 m) aggregation and disaggregation derived ET values were highly correlated with the measured eddy covariance fluxes within the source footprint area. Furthermore, both aggregation and disaggregation approaches demonstrated similar ET spatial patterns within the study area, with slight variations observed under the Landsat 30 m resolution. The results indicated that TsHARP downscaled high-resolution imagery provides optimal spatial resolution for mapping and analyzing ET at the plant to orchard scale, with a 95% confidence interval and an RMSE of 0.43 mm/day. Overall, the results suggest that high spatial resolution imagery disaggregated from Landsat was the best alternative for estimating ET fluxes from the plant to the orchard scale, which is beneficial for precision irrigation management in orchards, but the framework can also be applied to other precision agriculture applications.

Spatiotemporal analysis of drought by CHIRPS precipitation estimates

Drought is one of the most devastating natural hazards causing considerable losses in all climatic zones of the world. It is one of the most complex and the least understood hazards at the same time because of its spatially heterogeneous and temporally variable character. Spatially dense and uniformly distributed ground-based meteorological data are needed for proper spatial and temporal drought analysis. In practice, such data are lacking in general due either to the nonexistence of ground stations or their uneven and scarce distribution over a region. This creates a great potential in the use of satellite precipitation estimates (SPEs) such as the long-period high-resolution Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data in drought analysis. In this study, we aim to analyze drought over the Kucuk Menderes River Basin in the western part of Turkey by using the CHIRPS data, which were found highly correlated with precipitation in the local ground stations. The analysis was performed by considering the spatial variability and temporal change in the drought characterization based on the Standardized Precipitation Index (SPI) calculated at the 3-month (seasonal) timescale. Drought in the river basin was found to have a within-year variability from month to month, and a spatial variability over the basin in any given month. Also, an over-year variability with a decreasing trend exists, which could be considered a signal for more strengthened droughts in the future. The study eventually demonstrates how the CHIRPS SPEs could be useful in the spatial and temporal drought analysis for regions with limited ground-based meteorological data.

Evapotranspiration Estimation Using Remote Sensing Technology Based on a SEBAL Model in the Upper Reaches of the Huaihe River Basin

Understanding the spatial and temporal variations of evapotranspiration (ET) is vital for water resources planning and management and drought monitoring. The development of a satellite remote sensing technique is described to provide insight into the estimation of ET at a regional scale. In this study, the Surface Energy Balance Algorithm for Land (SEBAL) was used to calculate the actual ET on a daily scale from Landsat-8 data and daily ground-based meteorological data in the upper reaches of Huaihe River on 20 November 2013, 16 April 2015 and 23 March 2018. In order to evaluate the performance of the SEBAL model, the daily SEBAL ET (ETSEBAL) was compared against the daily reference ET (ET0) from four theoretical methods: the Penman-Monteith (P-M), Irmak-Allen (I-A), the Turc, and Jensen-Haise (J-H) method, the ETMOD16 product from the MODerate Resolution Imaging Spectrometer (MOD16) and the ETVIC from Variable Infiltration Capacity Model (VIC). A linear regression equation and statistical indices were used to model performance evaluation. The results showed that the daily ETSEBAL correlated very well with the ET0, ETMOD16, and ETVIC, and bias between the ETSEBAL with them was less than 1.5%. In general, the SEBAL model could provide good estimations in daily ET over the study region. In addition, the spatial-temporal distribution of ETSEBAL was explored. The variation of ETSEBAL was significant in seasons with high values during the growth period of vegetation in March and April and low values in November. Spatially, the daily ETSEBAL values in the mountain area were much higher than those in the plain areas over the study region. The variability of ETSEBAL in this study area was positively correlated with elevation and negatively correlated with surface reflectance, which implies that elevation and surface reflectance are the important factors for predicting ET in this study area.

English

Kajiado County is mainly Arid and Semi-Arid Lands (ASALs) and as such it is faced with harsh climatic conditions. The county is dominantly inhabited by the Maasai pastoralists who depend on climate-sensitive natural resources for their provisions and production needs. As such, they are faced with low productivity. Therefore, the study investigated historical temperature characteristics combined with surveys on the Maasai pastoralists’ experiences. The study used a mixed-method approach using temperature data along with semi-structured questionnaires. Temperature analysis utilised a mix of ground-based meteorological and satellite data. These temperature data were retrieved from Kenya Meteorological Department (KMD) and Climate Hazards Infra-Red Temperature with Stations (CHIRTS) respectively. The climatology analysis from the T-test showed that both the maximum and minimum temperature increased significantly at p < 0.05. A similar temperature rise was noted for spatial analysis where an overall warming trend was observed across the entire county. The Chi-square analysis affirmed the adverse effects of the temperature rise on the Maasai pastoralists as indicated in the growing numbers of heat-related illnesses and deaths in livestock. The resultant social-economic impacts translated to a decline in livestock population pointing up to the need for enhanced preparedness for the warming trends and better prediction models to enhance both societal and ecosystem resilience. Key words: Temperature, natural resources, pastoral livelihood system, society, ecosystem, resilience, climate risks.

Evaluating the dynamics of grassland net primary productivity in response to climate change in China

Net primary productivity (NPP) has been considered as a direct indicator to access the terrestrial primary production. However, considerable uncertainties remain toward the response mechanism of NPP to climate change across annual and seasonal scales, especially for grassland ecosystems. In this study, the spatiotemporal dynamics of grassland NPP and its prominent climate-controlling factor were evaluated based on the CASA model and ground-based meteorological data across annual and seasonal scales in China from 1982 to 2016. Results showed that the annual mean grassland NPP was 294.58 gC·m−2·a−1 over the 35 years; the largest annual mean NPP was observed in Alpine sub-alpine meadow (235.72 ± 13.21 gC·m−2·a−1), while the smallest annual mean NPP value occurred in Desert grassland (97.27 ± 9.64 gC·m−2·a−1). Moreover, the multiyear mean grassland NPP showed a decreasing trend from the southeast to the northwest spatially. Temporally, grassland NPP exhibited a significant increasing trend (slope = 2.4 gC·m−2·a−1; p < 0.0001) over the whole study period; an abrupt change point across the grassland NPP time series was observed in 1999. Specially, 66.34% of the grassland area showed an increasing trend in NPP, and the areas with a significant increasing trend were concentrated in Qinghai-Tibet Plateau and the northern area of Xinjiang. Further climate factor analysis with NPP indicated that NPP positively correlated with precipitation and negatively correlated with temperature for the whole grassland area. In particular, the temperature was a dominant driver in NPP variations for Alpine sub-alpine meadow, Slope grassland and Alpine sub-alpine grassland, while precipitation controlled the most in NPP variations for Plain grassland and Desert grassland. The accumulation of grassland NPP in spring and autumn benefits from the increase in temperature, and precipitation promoted the accumulated NPP in summer and winter.

Simulation effect evaluation of single-outlet and multi-outlet calibration of Soil and Water Assessment Tool model driven by Climate Forecast System Reanalysis data and ground-based meteorological station data – a case study in a Yellow River source

Abstract It is the research hotspot in the field of hydrology to apply the climate model and its downscaling data into hydrological simulations, and it is very important to evaluate the accuracy of its data. In this study, the accuracy of Climate Forecast System Reanalysis (CFSR) was evaluated from two perspectives: statistical evaluation and hydrological evaluation. In the hydrological evaluation, the applicability of CFSR in the Soil and Water Assessment Tool (SWAT) model of the Yellow River source area was studied. The results show that CFSR temperature data at the source of the Yellow River is consistent with the measured temperature data, and CFSR precipitation data overestimates precipitation. In the Yellow River source runoff simulation, the SWAT model driven by CFSR can obtain satisfactory simulation results. It does not reduce the simulation accuracy at the total outlet of the basin under the multi-outlet calibration method. It also considers the spatial differences of hydrological characteristics of each sub-basin and improve the simulation accuracy of the sub-basin simulation.

Positive effects of ecological restoration policies on the vegetation dynamics in a typical ecologically vulnerable area of China

Ecological restoration programmes have been initiated worldwide because of serious environmental degradation. However, the effectiveness of ecological restoration remains contentious because vegetation restoration is impacted by both climate change and human activities. Climate change is an important factor for vegetation restoration, especially in ecologically vulnerable areas. The Zhangjiakou region of northern China is located in a semiarid and subhumid climate region with apparent ecosystem vulnerability. Since the late 1970s, a series of ecological restoration programmes have been carried out in this area to recover the vegetation and ecosystems. Based on remote sensing images and ground-based meteorological data, two vegetation indexes (NDVI and EVI2) and aridity index were calculated to analyse (i) the vegetation dynamics and climate aridity trends in the Zhangjiakou region and (ii) the response of the vegetation dynamics to ecological restoration programmes and climate change between 1987 and 2017. The results indicated that vegetation cover increased with twice abrupt changes in 2002 and 2009, respectively, which simultaneously underwent an increase in aridity. Vegetation cover has increased rapidly since 2003 because of ecological recovery projects and policies. The contributions of climate change and human activities to vegetation cover were12%~ 5.2% and 88% ~ 94.8% during the period 2003–2009, respectively. The contributions of climate change and human activities to vegetation cover were2.1% ~ 2.4% and 97.9% ~ 97.6% during the period 2010–2017, respectively. The ecological restoration projects and policies effectively promoted vegetation cover in the study area.

Long-term trends and variability in the microclimates of the uMngeni Catchment, KwaZulu-Natal, South Africa and potential impacts on water resources

The past few decades have produced a wealth of research investigating the extent and severity of global climate change. Patterns in climate variability and change have been studied at global, continental and regional scales. Trends observed at these larger spatial scales are expected to be observable at smaller scales, although research in this regard has been limited in South Africa. This study presents an investigation into the microclimatic trends of a key catchment in the KwaZulu-Natal province, South Africa, with particular focus on water resources. Long-term (1980–2017) historical daily meteorological data were analysed using the RClimDex software package as well as other multi-temporal regression and time series analyses. Results indicate an overall warming of the study sites between 1 and 4 °C century−1. Atmospheric water vapour pressure was found to be increasing significantly at three of the sites. Total annual rainfall, an important variable for water resources of the catchment, did not display significant change in the catchment but high levels of inter-annual variability were observed. The results of the study indicate that the catchment microclimate has changed, and is changing, but at different rates from global and continental estimates. The study emphasises the importance of catchment scale analyses using ground-based historical meteorological data.

A Comparison of the Accuracy of Multi-satellite Precipitation Estimation and Ground Meteorological Records Over Southwestern Nigeria

Multi-satellite rainfall observations are compared with ground-based meteorological stations data in this study. Both ground and satellite meteorological datasets were analysed and synchronized in this study. Satellite rainfall datasets used are Tropical Rainfall Measuring Mission (TRMM), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) and African Rainfall Estimation (RFE 2.0), which were obtained from the archives of the NOAA Climate Prediction Centre. The ground meteorological data were obtained from the Nigerian Meteorological Agency (NIMET) Lagos office in Oshodi, for selected stations in Southwest of Nigeria, for the period 1998–2016. Descriptive and inferential analyses were used to analyse the dataset. Spatial and temporal trends analyses were carried out to compare rainfall estimates from satellite and ground stations. Generally, correlation results show that the relationship between satellite datasets and ground observation (NIMET) appears remarkably high in all stations with R2> 0.70. This relationship is greatly strong between TRMM and NIMET in Abeokuta, Akure, and Osogbo with R2 = 0.99, 0.98 and 0.98, respectively. CHIRPS also show high correlation with NIMET in these three stations with R2> 0.90, but RFE-NIMET relationship appears much more in Osogbo with R2 > 0.90. It is also obvious that TRMM and CHIRPS have a very high relationship in almost all locations with R2> 0.87. The major finding from this study is that though in situ meteorological data may be used for local studies of small areas, satellite rainfall estimates may also be used to augment such data, over a larger area. Besides, satellite rainfall estimates are more useful for areas with larger spatial and temporal coverage.

Examination of in-cloud sulfate chemistry using a different model initialization

We first examine characteristics of in-cloud sulfate chemistry of a mid-latitude convective cloud using a different model initialization. Specifically, we studied the relative importance of convection on sulfate aerosol transport and redistribution. The analysis is focused on 4 April 2000, when considerable dust transport is evidenced with high concentration and wet deposition of sulfate aerosol measured at the European Monitoring Environmental Program (EMEP) station located in the western mountain part of the Republic of Macedonia. The cloud-resolving model coupled with aqueous sulfate chemistry module has been employed to simulate the sulfate aerosol aqueous chemistry and wet deposition. The modeled results and sensitivities under various initializations are evaluated and compared with a ground-based measurements and laboratory analysis. The results indicate that the local environmental profiles derived from 2.5 km-scale WRF model provides a better cloud model initialization, capable of realistic representation of sub-grid-scale processes which are not adequately resolved by the WRF model with a coarser grid resolution. The method also shows a relatively good performance in simulation of cloud physical processes which take place in-cloud and the near cloud environment, cloud-chemistry interactions, relevant to sulfate acidification and sulfate deposition, which allows a more accurate quantitative assessment of sulfate concentration and pH values. Analysis also indicated that scavenging and oxidation are the principal processes affecting sulfate production, participating with 33 and 46%, respectively. Turning off the ice-phase processes leads to overprediction of sulfate aerosol production for about 8% relative to the base run. The verification analysis indicates that the numerical simulation initialized with a 2.5-km-scale WRF model shows a higher correlation coefficient with observations compared to other runs. This approach of initialization based on WRF conditions provides a scientific contribution by evaluating simulations of convective clouds in Macedonia against ground-based meteorological and chemical data, as well as by using the model to understand the driving processes affecting sulfate production and wet deposition.

The relationship between drought activity and vegetation cover in Northwest China from 1982 to 2013

We investigated drought activity and the relationship between drought and vegetation in Northwest China over the period 1982–2013 using the standardized precipitation evapotranspiration index (SPEI) and the normalized difference vegetation index (NDVI). The indexes were, respectively, calculated from ground-based meteorological data and from remotely sensed satellite data. The spatial and temporal distributions of drought (SPEI) and of vegetation cover (NDVI) were compared using annual trends, and the relationships between these trends were analyzed. The results are: (1) Overall, Northwest China had a drought trend during the study period, although some a few regions show a significant wetness trend; (2) the mean annual NDVI fluctuates, but overall shows an increasing trend, particularly in some mountainous areas that have at least adequate water and vegetation cover, while unused land becomes degraded; (3) most regions show a positive correlation between SPEI and NDVI, although the western parts of the Tarim basin, Qaidam basin, and some regions in the southeastern part of study area show a negative correlation; and (4) the various regions respond differently to global climate change, but in general regions with more vegetation cover show increased vegetation growth, while regions with less vegetation cover are becoming degraded and thus more vulnerable to the adverse effects of climate change.

Monitoring growth condition of spring maize in Northeast China using a process-based model

Early and accurate assessment of the growth condition of spring maize, a major crop in China, is important for the national food security. This study used a process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model, driven by satellite-derived leaf area index and ground-based meteorological observations, to simulate net primary productivity (NPP) of spring maize in Northeast China from the first ten-day (FTD) of May to the second ten-day (STD) of August during 2001–2014. The growth condition of spring maize in 2014 in Northeast China was monitored and evaluated spatially and temporally by comparison with 5- and 13-year averages, as well as 2009 and 2013. Results showed that NPP simulated by the RS-P-YEC model, with consideration of multi-scattered radiation inside the crop canopy, could reveal the growth condition of spring maize more reasonably than the Boreal Ecosystem Productivity Simulator. Moreover, NPP outperformed other commonly used vegetation indices (e.g., Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI)) for monitoring and evaluating the growth condition of spring maize. Compared with the 5- and 13-year averages, the growth condition of spring maize in 2014 was worse before the STD of June and after the FTD of August, and it was better from the third ten-day (TTD) of June to the TTD of July across Northeast China. Spatially, regions with slightly worse and worse growth conditions in the STD of August 2014 were concentrated mainly in central Northeast China, and they accounted for about half of the production area of spring maize in Northeast China. This study confirms that NPP is a good indicator for monitoring and evaluating growth condition because of its capacity to reflect the physiological characteristics of crops. Meanwhile, the RS-P-YEC model, driven by remote sensing and ground-based meteorological data, is effective for monitoring crop growth condition over large areas in a near real time.

Mapping Annual Riparian Water Use Based on the Single-Satellite-Scene Approach

The accurate estimation of water use by groundwater-dependent riparian vegetation is of great importance to sustainable water resource management in arid/semi-arid regions. Remote sensing methods can be effective in this regard, as they capture the inherent spatial variability in riparian ecosystems. The single-satellite-scene (SSS) method uses a derivation of the Normalized Difference Vegetation Index (NDVI) from a single space-borne image during the peak growing season and minimal ground-based meteorological data to estimate the annual riparian water use on a distributed basis. This method was applied to a riparian ecosystem dominated by tamarisk along a section of the lower Colorado River in southern California. The results were compared against the estimates of a previously validated remotely sensed energy balance model for the year 2008 at two different spatial scales. A pixel-wide comparison showed good correlation (R2 = 0.86), with a mean residual error of less than 104 mm∙year−1 (18%). This error reduced to less than 95 mm∙year−1 (15%) when larger areas were used in comparisons. In addition, the accuracy improved significantly when areas with no and low vegetation cover were excluded from the analysis. The SSS method was then applied to estimate the riparian water use for a 23-year period (1988–2010). The average annual water use over this period was 748 mm∙year−1 for the entire study area, with large spatial variability depending on vegetation density. Comparisons with two independent water use estimates showed significant differences. The MODIS evapotranspiration product (MOD16) was 82% smaller, and the crop-coefficient approach employed by the US Bureau of Reclamation was 96% larger, than that from the SSS method on average.

Detection and assessment of cloud cover and precipitation parameters using data of scanning radiometers of polar–orbiting and geostationary meteorological satellites

Two multispectral threshold techniques have been developed and tested for the automatic classification of AVHRR/NOAA and SEVIRI/Meteosat-10 data. They provide day-and-night detection and the assessment of cloud-cover parameters, as well as the discrimination of precipitation zones and severe weather phenomena. The validation of output information products, which has been performed with ground-based conventional meteorological observations and radar data, as well as with independent satellite-based estimates of cloud cover and precipitation parameters, confirms the feasibility of developed techniques and reasonable accuracy of output products. Therefore, the technique is concurrent to those implemented in current foreign satellite centers.

Producing cloud-free MODIS snow cover products with conditional probability interpolation and meteorological data

Abstract Cloud cover and snow misclassifications are the two major limitations for the hydrological application of MODIS snow data. Ground-based meteorological data have the inherent potential to provide the means to reconstruct snow cover for regions in which MODIS snow maps are obstructed by clouds, and to reduce misclassified snow observations. In this study, a multistep method is developed to generate cloud-free MODIS daily snow cover products. The accuracy of the updated MODIS snow products is evaluated for a region in southwestern Germany in which winter cloud coverage and snow variability are typically high. First, we applied Aqua/Terra combination, temporal combination and spatial combination to reduce the cloud coverage and to retrieve the omitted snow. This procedure was not effective since cloud blockage occurs frequently during the snow season. A conditional probability interpolation was then employed to reclassify the remaining cloud cover on MODIS snow maps based on in situ snow depth observations. Finally, we implemented a set of meteorological filters to minimize the misclassified snow in MODIS snow products. The improved cloud-free MODIS daily snow maps showed an overall accuracy of about 92% during the snow season with significantly reduced snow overestimation errors and a slight increase in snow omission errors, compared to the overall accuracy of 87% and 94% for original MODIS Aqua and Terra data, respectively. This study suggests that the fusion of ground-based and satellite-based snow observations is an effective approach for generating cloud-free remote sensing snow data.

Effective monitoring and warning of Urban Heat Island effect on the indoor thermal risk in Bucharest (Romania)

Extreme hot events and heat waves occur frequently in Bucharest during the warm season, triggering significant heat stress and thermal risks, especially in buildings with inappropriate ventilation, while climate change scenarios agree upon the warming trend along the next decades. This study investigates the impact of the Urban Heat Island (UHI) on the thermal regime of buildings, in order to develop a warning system capable to issue early warnings when the thermal risk reaches high levels in Bucharest. The warnings should be accurate regarding the intensity of the risk, the temporal fit and location, and complex information is compiled (e.g. air and land surface temperature, land cover, buildings and flat characteristics). Ground-based meteorological data and satellite products were used for computing the ambient temperature over several test areas for the summer months, and the indoor climate was dynamically modelled with an hourly resolution. The thermal risk was determined using standardized comfort indices, e.g. Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), and the specific thermal and functional characteristics of the buildings.

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead “tendone” systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the “direct” methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm·d−1, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013, and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.