Ground Weather Stations Research Articles

Spatial Modeling of Mosquito Vectors for Rift Valley Fever Virus in Northern Senegal: Integrating Satellite-Derived Meteorological Estimates in Population Dynamics Models

Mosquitoes are vectors of major pathogen agents worldwide. Population dynamics models are useful tools to understand and predict mosquito abundances in space and time. To be used as forecasting tools over large areas, such models could benefit from integrating remote sensing data that describe the meteorological and environmental conditions driving mosquito population dynamics. The main objective of this study is to assess a process-based modeling framework for mosquito population dynamics using satellite-derived meteorological estimates as input variables. A generic weather-driven model of mosquito population dynamics was applied to Rift Valley fever vector species in northern Senegal, with rainfall, temperature, and humidity as inputs. The model outputs using meteorological data from ground weather station vs satellite-based estimates are compared, using longitudinal mosquito trapping data for validation at local scale in three different ecosystems. Model predictions were consistent with field entomological data on adult abundance, with a better fit between predicted and observed abundances for the Sahelian Ferlo ecosystem, and for the models using in-situ weather data as input. Based on satellite-derived rainfall and temperature data, dynamic maps of three potential Rift Valley fever vector species were then produced at regional scale on a weekly basis. When direct weather measurements are sparse, these resulting maps should be used to support policy-makers in optimizing surveillance and control interventions of Rift Valley fever in Senegal.

A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data

Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1 . 23 K for Tmax and 1 . 20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1 . 21 K for Tmax and 1 . 27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time.

Improving agricultural microinsurance by applying universal kriging and generalised additive models for interpolation of mean daily temperature

Agricultural microinsurance has the potential to protect farmers against crop loss caused by extreme adverse weather conditions. Microinsurance policies for smallholder farmers are often designed on the basis of weather indices, whereby weather insurance variables are measured at ground weather stations and then interpolated to the location of the farm. However, a low density of weather stations causes interpolation error, which contributes to basis risk. The objective of this paper is to investigate whether agricultural microinsurance can be improved by reducing interpolation error through advanced interpolation methods, including universal kriging (UK) and generalised additive models (GAM) used with land surface temperature, elevation, and other covariates. Results indicate that for areas with a lower density of weather stations, UK with elevation substantially improves air temperature interpolation accuracy. The approach developed in this paper may help to improve interpolation and could therefore reduce basis risk for agricultural microinsurance in regions with a low density of weather stations, such as in developing countries.

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.

Remote Sensing Applications for Insurance: A Predictive Model for Pasture Yield in the Presence of Systemic Weather

A robust predictive model for crop yield is essential for designing a commercially viable index-based insurance policy. Index insurance for crops is still at a relatively infant stage, and more research and development is needed in order to address a main limitation, which is referred to as basis risk (the mismatch between the loss indicated by the index and the actual loss suffered by the insured). Traditionally ground weather station measurements have been the most common approach used in weather indices, and this approach has often led to high levels of basis risk. Recent advances in satellite-based remote sensing provides new opportunities to use publicly available and transparent “big data,” to potentially make index-based insurance policies more relevant by reducing basis risk. This is the first paper to provide a comprehensive comparison of thirteen Pasture Production Indices (PPI’s), including those developed based on satellite-derived vegetation and biophysical parameter indices using data products from the Moderate Resolution Imaging Spectroradiometer - MODIS. A validation protocol is established, and a unique dataset covering the period from 2002 to 2016 from a network of pasture clip sites in the province of Alberta, Canada is used to demonstrate new applications for insurance based on remote sensing derived data. The results of the satellite-deriverd PPI’s are compared to those PPI’s based on ground weather station data as benchmarks, which to date are the most common design for index insurance. Overall, the satellite-based indices report higher correlations with the ground truth forage yield data compared to the weather station PPI’s. As an example, in 2015 and 2016 the best performing satellite-based PPI’s produce correlations of close to 90%. When considering the whole sample period, the highest overall average correlation is 62.0% for the biophysical parameter PPI based on FPAR 500 meter MODIS, and the lowest overall average correlation is 43.8% for the vegetation PPI based on EVI 250 meter MODIS. Comparatively, the highest correlation reported for the weather station indices is for precipitation at 12.51%. A straightforward, yet effective PCA-based prediction model is introduced in this paper to address the issues of variable selection and dimension reduction in insurance design. This research makes an important contribution to the field of actuarial science and insurance, as it highlights potential new opportunities for insurance design and predictive analytics using large and comprehensive satellite datasets that remain relatively unexplored to date in insurance practice. While agriculture is used as the example here, the research could be extended to other crops, and other areas in the Property & Casualty sector, including for fire and flood.

Using Multiple Monthly Water Balance Models to Evaluate Gridded Precipitation Products over Peninsular Spain

The availability of precipitation data is the key driver in the application of hydrological models when simulating streamflow. Ground weather stations are regularly used to measure precipitation. However, spatial coverage is often limited in low-population areas and mountain areas. To overcome this limitation, gridded datasets from remote sensing have been widely used. This study evaluates four widely used global precipitation datasets (GPDs): The Tropical Rainfall Measuring Mission (TRMM) 3B43, the Climate Forecast System Reanalysis (CFSR), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and the Multi-Source Weighted-Ensemble Precipitation (MSWEP), against point gauge and gridded dataset observations using multiple monthly water balance models (MWBMs) in four different meso-scale basins that cover the main climatic zones of Peninsular Spain. The volumes of precipitation obtained from the GPDs tend to be smaller than those from the gauged data. Results underscore the superiority of the national gridded dataset, although the TRMM provides satisfactory results in simulating streamflow, reaching similar Nash-Sutcliffe values, between 0.70 and 0.95, and an average total volume error of 12% when using the GR2M model. The performance of GPDs highly depends on the climate, so that the more humid the watershed is, the better results can be achieved. The procedures used can be applied in regions with similar case studies to more accurately assess the resources within a system in which there is scarcity of recorded data available.

Estimation of Urban Air Temperature From a Rural Station Using Remotely Sensed Thermal Infrared Data

Growing urbanization over the past decades has increased energy consumption and vehicle usage across the world, which in turn has contributed to the phenomenon called urban heat island (UHI) effect. The most important variable to characterize UHI is the urban-rural air temperature differential. This study aims, generally, at deriving a correlation between air temperatures measured at ground weather stations and land surface temperatures estimated using remotely sensed thermal infrared data. Alternatively, we correlate the air temperature directly to the infrared data. Artificial neural network modelling is shown to be superior to other approaches. While weather stations can be costly to install and maintain, satellite images have become more accessible with technological advances and offer greater land coverage. It is therefore relevant to find the most accurate correlation in order to enable future studies to access air temperature values without the need for ground stations. Another important contribution is the assessment of the conditions of portability of the correlation model derived for one geographical location to another nearby location. This will enable to cover a large range of land with inputs from the same ‘reference’ ground station. The application of this correlation has demonstrated to be valid for high temperatures, which justifies its applicability in the Middle East region, and more so in Abu Dhabi where automobile and air conditioning usage is high, increasing the effects of UHI.

The influence of land-cover type on the relationship between NDVI–LST and LST-Tair

ABSTRACTAir temperature (T2m or Tair) measurements from 20 ground weather stations in Berlin were used to estimate the relationship between air temperature and the remotely sensed land surface temperature (LST) measured by Moderate Resolution Imaging Spectroradiometer over different land-cover types (LCT). Knowing this relationship enables a better understanding of the magnitude and pattern of Urban Heat Island (UHI), by considering the contribution of land cover in the formation of UHI. In order to understand the seasonal behaviour of this relationship, the influence of the normalized difference vegetation index (NDVI) as an indicator of degree of vegetation on LST over different LCT was investigated. In order to evaluate the influence of LCT, a regression analysis between LST and NDVI was made. The results demonstrate that the slope of regression depends on the LCT. It depicts a negative correlation between LST and NDVI over all LCTs. Our analysis indicates that the strength of correlations between LST and NDVI depends on the season, time of day, and land cover. This statistical analysis can also be used to assess the variation of the LST–T2m relationship during day- and night-time over different land covers. The results show that LSTDay and LSTNight are correlated significantly (p = 0.0001) with T2mDay (daytime air temperature) and T2mNight (night-time air temperature). The correlation (r) between LSTDay and TDay is higher in cold seasons than in warm seasons. Moreover, during cold seasons over every LCT, a higher correlation was observed during daytime than during night-time. In contrast, a reverse relationship was observed during warm seasons. It was found that in most cases, during daytime and in cold seasons, LST is lower than T2m. In warm seasons, however, a reverse relationship was observed over all land-cover types. In every season, LSTNight was lower than or close to T2mNight.

Remote Sensing Derivation of Land Surface Temperature for Insect Pest Monitoring

Temperature has major influence in insect development and outbreak. At present, the common method of collecting temperature information mainly relies on ground weather stations. However, this method is unfeasible for a large-scale area as weather stations distributions are sparse. This, however, can be compensated by the temperature measured through remote sensing satellites known as Land Surface Temperature (LST). Hence, this paper reviews the advantages and disadvantages of Thermal Infrared (TIR) and Microwave (MW) sensors for the acquisition of LST. This review will focus on the availability, suitability and adaptability of those sensors in providing LST for insect pest monitoring with the comparison being concentrated on their spatial and temporal characteristics, along with their accuracies.

Assessment of NASA/POWER satellite‐based weather system for Brazilian conditions and its impact on sugarcane yield simulation

ABSTRACTThe gridded database provided by National Aeronautics and Space Administration/Prediction of World Wide Energy Resources (NASA/POWER) presents a global coverage of complete weather data at horizontal resolution of 1° latitude–longitude, becoming a potential source for agrometeorological studies. Once Brazil is a country with continental dimensions and the major sugarcane world producer, and its density of ground weather stations suitable for an efficient agricultural planning is sparse, the objectives of this study were to test how robust is the NASA/POWER database through its comparison with the Brazilian ground weather stations network records (INMET) and to quantify the impacts on potential (Yp) and attainable (Yatt) sugarcane yield simulations when setting NASA/POWER as source of input weather data. The comparisons for weather data records and sugarcane yield simulations were carried out from 1997 to 2016. Statistical indices presented a satisfactory performance for average air temperature (R2 = 0.73; d = 0.91), minimum air temperature (R2 = 0.72; d = 0.91), maximum air temperature (R2 = 0.57; d = 0.84), solar radiation (SR) (R2 = 0.71; d = 0.92), sunshine hours (R2 = 0.68; d = 0.90) and reference evapotranspiration, when calculated through Priestley–Taylor (ETo‐PT) method (R2 = 0.76; d = 0.93). When the weather variables were aggregated and compared with a 10‐day time scale, a strong improvement of statistical indices was obtained. Yp presented root mean square error (RMSE) smaller than 10 t ha−1 while relative mean error (RME) ranged between ±10% for majority of grid cells, with exception for southern Brazil due to low and frost temperatures that satellite cannot capture accurately. Even NASA/POWER offering a relatively coarse grid size database and perhaps some regional data fitting would give better results at higher latitudes and elevation. The results found in this study proved that NASA/POWER products could be used as a source of climatic data for agricultural activities with a reasonable confidence for regional and national spatial scales.

The Convection, Aerosol, and Synoptic-Effects in the Tropics (CAST) Experiment: Building an Understanding of Multiscale Impacts on Caribbean Weather via Field Campaigns

Abstract Modulated by global-, continental-, regional-, and local-scale processes, convective precipitation in coastal tropical regions is paramount in maintaining the ecological balance and socioeconomic health within them. The western coast of the Caribbean island of Puerto Rico is ideal for observing local convective dynamics as interactions between complex processes involving orography, surface heating, land cover, and sea-breeze–trade wind convergence influence different rainfall climatologies across the island. A multiseason observational effort entitled the Convection, Aerosol, and Synoptic-Effects in the Tropics (CAST) experiment was undertaken using Puerto Rico as a test case, to improve the understanding of island-scale processes and their effects on precipitation. Puerto Rico has a wide network of observational instruments, including ground weather stations, soil moisture sensors, a Next Generation Weather Radar (NEXRAD), twice-daily radiosonde launches, and Aerosol Robotic Network (AERONET) sunphotometers. To achieve the goals of CAST, researchers from multiple institutions supplemented existing observational networks with additional radiosonde launches, three high-resolution radars, continuous ceilometer monitoring, and air sampling in western Puerto Rico to monitor convective precipitation events. Observations during three CAST measurement phases (22 June–10 July 2015, 6–22 February 2016, and 24 April–7 May 2016) captured the most extreme drought in recent history (summer 2015), in addition to anomalously wet early rainfall and dry-season (2016) phases. This short article presents an overview of CAST along with selected campaign data.

MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling

Species Distribution Models (SDMs) combine information on the geographic occurrence of species with environmental layers to estimate distributional ranges and have been extensively implemented to answer a wide array of applied ecological questions. Unfortunately, most global datasets available to parameterize SDMs consist of spatially interpolated climate surfaces obtained from ground weather station data and have omitted the Antarctic continent, a landmass covering c. 20% of the Southern Hemisphere and increasingly showing biological effects of global change. Here we introduce MERRAclim, a global set of satellite-based bioclimatic variables including Antarctica for the first time. MERRAclim consists of three datasets of 19 bioclimatic variables that have been built for each of the last three decades (1980s, 1990s and 2000s) using hourly data of 2 m temperature and specific humidity. We provide MERRAclim at three spatial resolutions (10 arc-minutes, 5 arc-minutes and 2.5 arc-minutes). These reanalysed data are comparable to widely used datasets based on ground station interpolations, but allow extending their geographical reach and SDM building in previously uncovered regions of the globe.

Quantifying the Spatiotemporal Trends of Canopy Layer Heat Island (CLHI) and Its Driving Factors over Wuhan, China with Satellite Remote Sensing

Canopy layer heat islands (CLHIs) in urban areas are a growing problem. In recent decades, the key issues have been how to monitor CLHIs at a large scale, and how to optimize the urban landscape to mitigate CLHIs. Taking the city of Wuhan as a case study, we examine the spatiotemporal trends of the CLHI along urban-rural gradients, including the intensity and footprint, based on satellite observations and ground weather station data. The results show that CLHI intensity (CLHII) decays exponentially and significantly along the urban-rural gradients, and the CLHI footprint varies substantially and especially in winter. We then quantify the driving factors of the CLHI by establishing multiple linear regression (MLR) models with the assistance of ZY-3 satellite data (with a spatial resolution of 2.5 m), and obtain five main findings: (1) built-up area had a significant positive effect on daily mean CLHII in summer and a negative effect in winter; (2) vegetation had significant inhibiting effects on daily mean CLHII in both summer and winter; (3) absolute humidity has a significant inhibiting effect on daily mean CLHII in summer and a positive effect in winter; (4) anthropogenic heat emissions exacerbated the daily mean CLHII by about 0.19 °C (90% confidence interval −0.06–0.44 °C) on 17 September 2013 and by about 0.06 °C (−0.06–0.19 °C) on 23 January 2014; and (5) if most of the urban area is transformed into roads (i.e., an extreme case), we estimate that the daily mean CLHII would reach 1.41 °C (0.38–2.44 °C) on 17 September 2013 and 0.14 °C (0.08–0.2 °C) on 23 January 2014 in Wuhan metropolitan area. Overall, the results provide new insights into quantifying the CLHI and its driving factors, to enhance our understanding of urban heat islands.

Onboard Wind Velocity Estimation Comparison for Unmanned Aircraft Systems

This paper presents a novel wind estimation approach, which is compared with existing ideas utilizing different combinations of common aircraft sensors to estimate the wind velocity in real time at the location of an aircraft. These different techniques were evaluated using simulation data as well as two experimental unmanned aircraft flight tests using validation data from a ground weather station. Significant performance advantage was shown of the new filtering technique over the existing approaches.

Variation in diurnal and seasonal urban land surface temperature: landuse change impacts assessment over Lagos metropolitan city

Removal of vegetation to give space for urban expansion might result in the temperature rise in cities. The present study compares the LSTs derived from Moderate Resolution Imaging Spectroradiometer (MODIS) with observed air temperature from ground weather data. The natures of the materials that are usually found in the urban area are typically concrete and asphalt materials which affect the urban atmospheric system. In this study, variation in urban land surface temperatures (LST) using MODIS and in-situ meteorological data were examined. MODIS data and daily rainfall, minimum (Tm) and maximum (Tmx) temperature from ground weather station were used. The results reveal that average LSTs during the dry season are noticeably higher for both daytime during November: 34.62 °C, December: 33.75 °C, January: 34.68 °C, February: 35.02 °C and March: 34.87 °C. There are notable differences in the LST observed between daytime and nighttime for both MOD11A2 and MYD11A2 and that of maximum and minimum air temperature from in-situ meteorological data. MOD11A2 is a better proxy for daily maximum and minimum air temperature than MYD11A2, though seasonal variations in the extent of LST occurs during the wet and dry season. The study shows that the contribution of the urban LSTs was comparatively smaller at night than the day, perhaps as result of the variations in the amount of solar radiation received by the day and night times.

Determinación de la humedad de suelo mediante regresión lineal múltiple con datos TerraSAR-X

<p>The first five centimeters of soil form an interface where the main heat fluxes exchanges between the land surface and the atmosphere occur. Besides ground measurements, remote sensing has proven to be an excellent tool for the monitoring of spatial and temporal distributed data of the most relevant Earth surface parameters including soil’s parameters. Indeed, active microwave sensors (Synthetic Aperture Radar - SAR) offer the opportunity to monitor soil moisture (HS) at global, regional and local scales by monitoring involved processes. Several inversion algorithms, that derive geophysical information as HS from SAR data, were developed. Many of them use electromagnetic models for simulating the backscattering coefficient and are based on statistical techniques, such as neural networks, inversion methods and regression models. Recent studies have shown that simple multiple regression techniques yield satisfactory results. The involved geophysical variables in these methodologies are descriptive of the soil structure, microwave characteristics and land use. Therefore, in this paper we aim at developing a multiple linear regression model to estimate HS on flat agricultural regions using TerraSAR-X satellite data and data from a ground weather station. The results show that the backscatter, the precipitation and the relative humidity are the explanatory variables of HS. The results obtained presented a RMSE of 5.4 and a R<sup>2</sup> of about 0.6</p>

Evaluating weather observations and the Climate Forecast System Reanalysis as inputs for hydrologic modelling in the tropics

AbstractCorrectly representing weather is critical to hydrological modelling, but scarce or poor quality observations can often compromise model accuracy. Reanalysis datasets may help to address this basic challenge. The Climate Forecast System Reanalysis (CFSR) dataset provides continuous, globally available records, and CFSR data have produced satisfactory hydrological model performance in some temperate and monsoonal locations. However, the use of CFSR for hydrological modelling in tropical and semi‐tropical basins has not been adequately evaluated. Taking advantage of exceptionally high rainfall station density in the catchments of the Rio Grande de Loiza above San Juan, Puerto Rico, we compared model performance based on CFSR records with that based on publicly available weather stations in the Global Historical Climate Network (GHCN, n = 21) and on a dataset of rainfall records maintained by the United States Geological Survey Caribbean Water Science Center (USGS, n = 24). For an implementation of the Soil and Water Assessment Tool (SWAT) with subbasins defined at 11 streamflow gages, uncalibrated measures of Nash–Sutcliffe efficiency (NSE) were >0 at 8 of 11 gages using USGS precipitation data for daily simulations over the period 1998–2012, but were <0 using GHCN weather station records (8 of 11) and CFSR reanalysis data (9 of 11). Autocalibration of individual SWAT models for each of the 11 basins against each of the available weather datasets yielded NSE values > 0 using all precipitation inputs, including CFSR. However, the ground weather station closest to the geographic basin centre produced the highest NSE values in only 5 of 11 cases. The spatially interpolated CFSR data performed as well or better than single ground observations made further than 20–30 km, and sometimes better than individual weather stations <10 km from the basin centroid. In addition to demonstrating the need to evaluate available weather inputs, this research reinforces the value of CFSR data as a means to supplement ground records and consistently determine a baseline for hydrologic model performance. Copyright © 2016 John Wiley & Sons, Ltd.

Determining the precipitable water vapor with ground-based GPS and comparing its yearly variation to rainfall over Taiwan

Water vapor plays an important role in weather prediction. Thus, it would be helpful to use precipitable water vapor (PWV) data from Global Positioning System (GPS) signals to understand weather phenomena. Approximately 100 ground GPS stations that cooperate with approximately 500 ground weather stations were used in this study. The relationship between the PWV and rainfall was investigated by analyzing the amplitude and phase that resulted from harmonic analyses. The results indicated that the maximum PWV amplitudes were between 10.98 and 13.10mm and always occurred at the end of July. The magnitudes of the PWV growth rate were between 0.65 and 0.81mm/yr. These rates increased from 9.2% to 13.0% between 2006 and 2011. The largest peak PWV amplitude occurred in the Western region. However, the largest rainfall amplitude occurred in the Southern region. The presented peak rainfall time agreed with the peak PWV time in the Western, Southern, and Central Mountain regions. Although rainfall decreased with time in Taiwan, this decrease was not large. The greatest rainfall consistently occurred during the months in which typhoons occurred, and the greatest PWV values occurred at the end of July. Although the end of July had the greatest monthly average PWV values, the rainfall magnitude during this period was smaller than that during the typhoons, which only occurred for a few days; the PWV also increased during typhoons. Because this effect was short-term, it did not significantly contribute to the PWV monthly average.

Weak precipitation, warm winters and springs impact glaciers of south slopes of Mt. Everest (central Himalaya) in the last 2 decades (1994–2013)

Abstract. Studies on recent climate trends from the Himalayan range are limited, and even completely absent at high elevation (> 5000 m a.s.l.). This study specifically explores the southern slopes of Mt. Everest, analyzing the time series of temperature and precipitation reconstructed from seven stations located between 2660 and 5600 m a.s.l. during 1994–2013, complemented with the data from all existing ground weather stations located on both sides of the mountain range (Koshi Basin) over the same period. Overall we find that the main and most significant increase in temperature is concentrated outside of the monsoon period. Above 5000 m a.s.l. the increasing trend in the time series of minimum temperature (+0.072 °C yr−1) is much stronger than of maximum temperature (+0.009 °C yr−1), while the mean temperature increased by +0.044 °C yr−1. Moreover, we note a substantial liquid precipitation weakening (−9.3 mm yr−1) during the monsoon season. The annual rate of decrease in precipitation at higher elevations is similar to the one at lower elevations on the southern side of the Koshi Basin, but the drier conditions of this remote environment make the fractional loss much more consistent (−47% during the monsoon period). Our results challenge the assumptions on whether temperature or precipitation is the main driver of recent glacier mass changes in the region. The main implications are the following: (1) the negative mass balances of glaciers observed in this region can be more ascribed to a decrease in accumulation (snowfall) than to an increase in surface melting; (2) the melting has only been favoured during winter and spring months and close to the glaciers terminus; (3) a decrease in the probability of snowfall (−10%) has made a significant impact only at glacier ablation zone, but the magnitude of this decrease is distinctly lower than the observed decrease in precipitation; (4) the decrease in accumulation could have caused the observed decrease in glacier flow velocity and the current stagnation of glacier termini, which in turn could have produced more melting under the debris glacier cover, leading to the formation of numerous supraglacial and proglacial lakes that have characterized the region in the last decades.

Wind Resource Potential in Los Taques-Venezuela

The Venezuelan government has established more aggressive policies and incentives for renewable energy resources in recent time, especially in terms of wind power. Although several academic efforts to make publically available wind energy resource data in Venezuela, there is a lack of information in terms of local wind resource putting in risk development in areas where potential is good enough for commercial exploitation. The objective of this paper is to presents a very comprehensive wind resource assessment at Los Taques, Venezuela based on on-site observation anemometry. This is unique paper because it is the first ever wind energy assessment in Los Taques using hourly data recorded during three years in an on-site ground weather station contrary to studies based on daily values based on radar or satellite data. The applied methodology has been developed based on the characteristic of the data obtained from the on-site anemometry. Results of wind energy assessment and evaluations on a 100 MW wind farm shows the wind energy resource available in Los Taques is enough for commercial use and the results.