No description available.

Mesonet stations are environmental monitoring systems used to examine environmental features such as Precipitation, Temperature, Insolation, Soil Moisture, and Localized Hazards. network of Mesonet stations is an integral part of weather and climate monitoring and the data produced are essential for agriculture, planning, transportation, and other industries. Tennessee is one of the few states without a Mesonet; a bill currently before the state assembly includes funding for such a system. Therefore, this research describes development of a Mesonet suitability layer for Tennessee that can be used to guide placement of individual Mesonet stations across the state, taking into account spacing, terrain, land use, construction suitability, and other factors. Land cover, road centerline, roughness, elevation, flood zone, building footprint, and soil type data were obtained from USGS, FEMA, NLCD, and the Tennessee state database to create each layer. Using ArcGIS Pro, each data layer was converted to a raster and then masked to the state of Tennessee at 30-meter resolution to ensure proper alignment of data overlays. The combined overlay map will identify zones that meet suitability criteria, and it is expected that multiple areas in each county will meet the such that a complete Mesonet network can be sited across the state. This study provides important information needed for final placement of Tennessee’s future Mesonet stations, which will provide essential weather and environmental monitoring data important for Tennessee.

No description available.

The snow cover plays an important role not only for the whole climate system but also for tourism and economy in the Lapland winter (e.g. dog sledding, snow mobile, etc). Snow constitutes a shelter for animals and plants during the winter due to thermal isolation, but, on the range of this investigation, it can make grazing difficult for reindeers, if the conditions are not favorable. Different approaches to the study have been made; the first and most important part of the investigation was a campaign in Järämä, in Swedish Lapland. During 3 days (between the 3rd and 5th of March 2009), a series of snow pits were done, recording snow grain size, snow layers depth, snow hardness/compactness, density and temperature. The hardness in the snow was evaluated through ram penetration tests. It was additionally studied the correspondence between the snow layers found in situ and the Sámi terminology. Another approach of the study consisted of satellite observations during the winter season 2008/2009 with day light in the region. The type of imagery used was MODIS (The Moderate Resolution Imaging Spectroradiometer) daily snow albedo and 8-day surface reflectance products. Measurements of temperature, precipitation, snow depth were used to cover the polar night time when satellite images were missing. According to these weather observations some snow metamorphisms were also studied, and their influence on the snowpack conditions. Through the comparison between all these forms of data it was found that in the winter season 2008/2009 the conditions for reindeers grazing were not good due to the formation of ice encapsulating the lichens and grass. Additionally several hard snow layers have been found in the snowpack which increase the difficulty to dig in the snow and may cause problems to the reindeers’ digestion. Snow hardness measurements with a ram penetrometer, manual tests and visual grain size observation proved these discovers. Several periods of positive temperature may cause melting/refreezing cycles contributing to the formation of hard snow layers. These conclusions are supported by the snow albedo and surface reflectance satellite imagery. In these images is visible a period with snow albedo decreasing a lot in the beginning of autumn, after the first lasting snowfall. The weather conditions in early fall, when the first durable snow occurs, are of extreme importance for the reindeers’ grazing, and in the case of the studied winter season, the conditions were not favorable.

Spatial distribution of precipitation in complex terrain can be very uneven and there are many factors to consider when studying it. The goal of this study was such a problem; to analyse the difference in observed annual precipitation in the complex terrain of Seyðisfjörður, a fjord in East-Iceland. The study was carried out in three parts. First, it was investigated if there was a systematic difference between measurement methods using different instruments. Second, the effect of the complex terrain on the spatial distribution of precipitation was investigated, and lastly if this observed difference could be linked to any certain kind of weather regimes. To simplify the analysis, only liquid precipitation was included in the data set.  In Seyðisfjörður there are three divergently located precipitation gauges of three different types and set up. At the Icelandic Meteorological Office in Reykjavík the same type of gauges are co-located with the exact same set up as in Seyðisfjörður. A statistical analysis of those measurements showed that there is a systematic undercatch in tipping bucket gauges compared to weighted capacity and standard accumulation gauges. However, the difference is insignificant in size compared to the observed difference in the complex terrain. The complex terrain was found to highly affect the airflow and therefore the spatial distribution of precipitation, as it almost only rains in synoptic wind directions with an easterly component (measured at a mountain station). To connect events where there was a great difference in precipitation measurements to the synoptic weather situation over the North-Atlantic, a projection connecting the geostrophic and local winds in the fjord was made. It showed that great precipitation as well as when great differences are observed, during two kind of weather regimes. One where a low pressure is travelling along the North-Atlantic storm track to the Norwegian Sea. The other were a low pressure is stationed southwest or west of Iceland in the Irminger Sea and a high pressure stretching up over Scandinavia. Convective precipitation makes up for a minimal part of the total precipitation and is not linked to events with great observed difference. Events with considerable precipitation but little observed difference are fewer and smaller than the events with much great observed precipitation and differences.

For the construction of a wind farm, one needs to assess the wind resources of the considered site location. Using reference time series from numerical weather prediction models, global assimilation databases or observations close to the area considered, the on-site measured wind speeds and wind directions are corrected in order to represent the actual long-term wind conditions. This long-term correction (LTC) is in the typical case performed by making use of the linear regression within the Measure-Correlate-Predict (MCP) method. This method and two other methods, Sector-Bin (SB) and Synthetic Time Series (ST), respectively, are used for the determination of the uncertainties that are associated with LTC.The test area that has been chosen in this work, is located in the region of the North Sea, using 22 quality controlled meteorological (met) station observations from offshore or nearby shore locations in Denmark, Norway and Sweden. The time series that has been used cover the eight year period from 2002 to 2009 and the year with the largest variability in the wind speeds, 2007, is used as the short-term measurement period. The long-term reference datasets that have been used are the Weather Research and Forecast model, based on both ECMWF Interim Re-Analysis (ERA-Interim) and National Centers for Environmental Prediction Final Analysis (NCEP/FNL), respectively and additional reference datasets of Modern Era Re-Analysis (MERRA) and QuikSCAT satellite observations. The long-term period for all of the reference datasets despite QuikSCAT, correspond to the one of stations observations. The QuikSCAT period of observations used cover the period from November 1st, 1999 until October 31st, 2009.The analysis is divided into three parts. Initially, the uncertainty connected to the corresponding reference dataset, when used in LTC method, is investigated. Thereafter the uncertainty due to the concurrent length of the on-site measurements and reference dataset is analyzed. Finally, the uncertainty is approached using a re-sampling method of the Non-Parametric Bootstrap. The uncertainty of the LTC method SB, for a fixed concurrent length of the datasets is assessed by this methodology, in an effort to create a generic model for the estimation of uncertainty in the predicted values for SB.The results show that LTC with WRF model datasets based on NCEP/FNL and ERA-Interim, respectively, is slightly different, but does not deviate considerably in comparison when comparing with met station observations. The results also suggest the use of MERRA reference dataset in connection with long-term correction methods. However, the datasets of QuikSCAT does not provide much information regarding the overall quality of long-term correction, and a different approach than using station coordinates for the withdrawal of QuikSCAT time series is preferred. Additionally, the LTC model of Sector-Bin is found to be robust against variation in the correlation coefficient between the concurrent datasets. For the uncertainty dependence of concurrent time, the results show that an on-site measurement period of one consistent year or more, gives the lowest uncertainties compared to measurements of shorter time. An additional observation is that the standard deviation of long-term corrected means decreases with concurrent time. Despite the efforts of using the re-sampling method of Non-Parametric Bootstrap the estimation of the uncertainties is not fully determined. However, it does give promising results that are suggested for investigation in further work. / För att bygga en vindkraftspark är man i behov av att kartlägga vindresurserna i det aktuella området. Med hjälp av tidsserier från numeriska vädermodeller (NWP), globala assimileringsdatabaser och intilliggande observationer korrigeras de uppmätta vindhastigheterna och vindriktningarna för att motsvara långtidsvärdena av vindförhållandena. Dessa långtidskorrigeringsmetoder (LTC) genomförs generellt sett med hjälp av linjär regression i Mät-korrelera-predikera-metoden (MCP). Denna metod, och två andra metoder, Sektor-bin (SB) och Syntetiska tidsserier (ST), används i denna rapport för att utreda de osäkerheter som är knutna till långtidskorrigering.Det testområde som är valt för analys i denna rapport omfattas av Nordsjöregionen, med 22 meteorologiska väderobservationsstationer i Danmark, Norge och Sverige. Dessa stationer är till största del belägna till havs eller vid kusten. Tidsserierna som används täcker åttaårsperioden från 2002 till 2009, där det året med högst variabilitet i uppmätt vindhastighet, år 2007, används som den korta mätperiod som blir föremål för långtidskorrigeringen. De långa referensdataseten som använts är väderprediktionsmodellen WRF ( Weather Research and Forecast Model), baserad både på data från NCEP/FNL (National Centers for Environmental Prediciton Final Analysis) och ERA-Interim (ECMWF Interim Re-analysis). Dessutom används även data från MERRA (Modern Era Re-Analysis) och satellitobservationer från QuikSCAT. Långtidsperioden för alla dataset utom QuikSCAT omfattar samma period som observationsstationerna. QuikSCAT-datat som använts omfattar perioden 1 november 1999 till 31 oktober 2009.Analysen är indelad i tre delar. Inledningsvis behandlas osäkerheten som är kopplad till referensdatans ingående i långtidskorrigeringsmetoderna. Därefter analyseras osäkerhetens beroende av längden på den samtidiga datan i referens- och observationsdataseten. Slutligen utreds osäkerheten med hjälp av en icke-parametrisk metod, en s.k. Bootstrap: Osäkerheten i SB-metoden för en fast samtidig längd av tidsserierna från observationer och referensdatat uppskattas genom att skapa en generell modell som estimerar osäkerheten i estimatet.Resultatet visar att skillnaden när man använder WRF-modellen baserad både på NCEP/FNL och ERA-Interim i långtidskorrigeringen är marginell och avviker inte markant i förhållande till stationsobservationerna. Resultatet pekar också på att MERRA-datat kan användas som långtidsreferensdataset i långtidsdkorrigeringsmetoderna. Däremot ger inte QuikSCAT-datasetet tillräckligt med information för att avgöra om det går att använda i långtidskorrigeringsmetoderna. Därför föreslås ett annat tillvägagångssätt än stationsspecifika koordinater vid val av koordinater lämpliga för långtidskorrigering. Ytterligare ett resultat vid analys av långtidskorrigeringsmetoden SB, visar att metoden är robust mot variation i korrelationskoefficienten.Rörande osäkerhetens beroende av längden på samtidig data visar resultaten att en sammanhängande mätperiod på ett år eller mer ger den lägsta osäkerheten i årsmedelvindsestimatet, i förhållande till mätningar av kortare slag. Man kan även se att standardavvikelsen av de långtidskorrigerade medelvärdena avtar med längden på det samtidiga datat. Den implementerade ickeparametriska metoden Bootstrap, som innefattar sampling med återläggning, kan inte estimera osäkerheten till fullo. Däremot ger den lovande resultat som föreslås för vidare arbete.