Comparison of Collocated Automated (NCECONet) and Manual (COOP) Climate Observations in North Carolina

2006 ◽  
Vol 23 (5) ◽  
pp. 671-682 ◽  
Author(s):  
Christopher Holder ◽  
Ryan Boyles ◽  
Ameenulla Syed ◽  
Dev Niyogi ◽  
Sethu Raman
Keyword(s):  
North Carolina ◽  
Climate Models ◽  
Daily Rainfall ◽  
Maximum Temperature ◽  
Spatial Density ◽  
Climate Data ◽  
Data Resource ◽  
Weather And Climate ◽  
Observation Times ◽  
Mean Square Errors

Abstract The National Weather Service's Cooperative Observer Program (COOP) is a valuable climate data resource that provides manually observed information on temperature and precipitation across the nation. These data are part of the climate dataset and continue to be used in evaluating weather and climate models. Increasingly, weather and climate information is also available from automated weather stations. A comparison between these two observing methods is performed in North Carolina, where 13 of these stations are collocated. Results indicate that, without correcting the data for differing observation times, daily temperature observations are generally in good agreement (0.96 Pearson product–moment correlation for minimum temperature, 0.89 for maximum temperature). Daily rainfall values recorded by the two different systems correlate poorly (0.44), but the correlations are improved (to 0.91) when corrections are made for the differences in observation times between the COOP and automated stations. Daily rainfall correlations especially improve with rainfall amounts less than 50 mm day−1. Temperature and rainfall have high correlation (nearly 1.00 for maximum and minimum temperatures, 0.97 for rainfall) when monthly averages are used. Differences of the data between the two platforms consistently indicate that COOP instruments may be recording warmer maximum temperatures, cooler minimum temperatures, and larger amounts of rainfall, especially with higher rainfall rates. Root-mean-square errors are reduced by up to 71% with the day-shift and hourly corrections. This study shows that COOP and automated data [such as from the North Carolina Environment and Climate Observing Network (NCECONet)] can, with simple corrections, be used in conjunction for various climate analysis applications such as climate change and site-to-site comparisons. This allows a higher spatial density of data and a larger density of environmental parameters, thus potentially improving the accuracy of the data that are relayed to the public and used in climate studies.

A new empirical model of sub-daily rainfall intensity and its application in a rangeland biophysical model

2011 ◽  
Vol 33 (1) ◽  
pp. 37 ◽  
Author(s):  
G. W. Fraser ◽  
J. O. Carter ◽  
G. M. McKeon ◽  
K. A. Day
Keyword(s):  
Empirical Model ◽  
Rainfall Intensity ◽  
Daily Rainfall ◽  
Biophysical Model ◽  
Maximum Temperature ◽  
Validation Dataset ◽  
Daily Maximum ◽  
Climate Data ◽  
Erosion Rates ◽  
Biophysical Models

Sub-daily rainfall intensity has a significant impact on runoff and erosion rates in northern Australian rangelands. However, it has been difficult to include sub-daily rainfall intensity in rangeland biophysical models using historical climate data due to the limited number of pluviograph stations with long-term records. In this paper a new empirical model (‘Temperature I15’ model) was developed to predict the daily maximum 15-min rainfall intensity (I15) using daily minimum and maximum temperature and daily rainfall totals from 12 selected pluviograph stations across Australia. The ‘Temperature I15’ model accounted for 46% (P < 0.01) of the variation in observed daily I15 for an independent validation dataset derived from 67 Australia-wide pluviograph stations and represented both geographical and seasonal variability in I15. The model also accounted for 70% (P < 0.01) of the variation in the observed historical trend in I15 for the full record period (average record period was 37 years) of 73 Australia-wide pluviograph stations. The ‘Temperature I15’ model was found to be an improvement on a past empirical model of I15 and can be easily implemented in biophysical models by using readily available daily climate data. However, as the ‘Temperature I15’ model only represented 46% of the variation in daily observed I15, the model is best used in simulation studies on ‘timeframes’ in excess of 5 years. The new ‘Temperature I15’ model was implemented in the runoff equation of the Australia-wide spatial pasture growth model AussieGRASS, which predicts daily water balance and pasture growth for 185 different pasture communities. This resulted in an improved simulation of green cover for 71% of pasture communities but was worse for 25% of communities, with no change for 4% of communities.

scholarly journals WegenerNet high-resolution weather and climate data from 2007 to 2020

2021 ◽  
Vol 13 (3) ◽  
pp. 1307-1334
Author(s):  
Jürgen Fuchsberger ◽  
Gottfried Kirchengast ◽  
Thomas Kabas
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Spatial Density ◽  
Wind Gust ◽  
Climate Data ◽  
Climate Change Research ◽  
Near Surface ◽  
Temporal Sampling ◽  
Weather And Climate ◽  
Data Level

Abstract. This paper describes the latest reprocessed data record (version 7.1) over 2007 to 2020 from the WegenerNet climate station networks, which since 2007 have been providing measurements with very high spatial and temporal resolution of hydrometeorological variables for two regions in the state of Styria, southeastern Austria: (1) the WegenerNet Feldbach Region, in the Alpine forelands of southeastern Styria, which extends over an area of about 22 km × 16 km and comprises 155 meteorological stations placed on a tightly spaced grid with an average spatial density of 1 station per ∼ 2 km2 and a temporal sampling of 5 min, and (2) the WegenerNet Johnsbachtal, which is a smaller “sister network” of the WegenerNet Feldbach Region in the mountainous Alpine region of upper Styria that extends over an area of about 16 km × 17 km and comprises 13 meteorological stations and 1 hydrographic station at altitudes ranging from below 600 m to over 2100 m and with a temporal sampling of 10 min. These networks operate on a long-term basis and continuously provide quality-controlled station time series for a multitude of hydrometeorological near-surface and surface variables, including air temperature, relative humidity, precipitation, wind speed and direction, wind gust speed and direction, soil moisture, soil temperature, and others like pressure and radiation variables at a few reference stations. In addition, gridded data are available at a resolution of 200 m × 200 m for air temperature, relative humidity, precipitation, and heat index for the Feldbach region and at a resolution of 100 m × 100 m for the wind parameters for both regions. Here we describe this dataset (the most recent reprocessing version 7.1) in terms of the measurement site and station characteristics as well as the data processing, from raw data (level 0) via quality-controlled basic station data (level 1) to weather and climate data products (level 2). In order to showcase the practical utility of the data, we also include two illustrative example applications, briefly summarize and refer to scientific uses in a range of previous studies, and briefly inform about the most recent WegenerNet advancements in 2020 towards a 3D open-air laboratory for climate change research. The dataset is published as part of the University of Graz Wegener Center's WegenerNet data repository under the DOI https://doi.org/10.25364/WEGC/WPS7.1:2021.1 (Fuchsberger et al., 2021) and is continuously extended.

scholarly journals Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

2021 ◽  
Vol 14 (3) ◽  
pp. 1267-1293
Author(s):  
Sara Top ◽  
Lola Kotova ◽  
Lesley De Cruz ◽  
Svetlana Aniskevich ◽  
Leonid Bobylev ◽  
...  
Keyword(s):  
High Resolution ◽  
Central Asia ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
The Tibetan Plateau ◽  
Climate Information ◽  
Climate Data

Abstract. To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

scholarly journals Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

2018 ◽  
Author(s):  
Christoph Schlager ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger
Keyword(s):  
High Resolution ◽  
Wind Field ◽  
Climate Models ◽  
Meteorological Station ◽  
Strong Wind ◽  
Climate Data ◽  
Wind Fields ◽  
Station Network ◽  
Weather And Climate ◽  
Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real time, recently developed by the authors (Schlager et al., 2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains eleven meteorological stations at elevations from about 600 m to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 minutes from the WegenerNet JBT, mean wind and wind gust fields at 10 m and 50 m height levels with a high spatial resolution of 100 × 100 m and a temporal resolution of 30 minutes. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. A main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance with somewhat better values for the strong wind conditions. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from five-year climate data of the WegenerNet JBT in comparison to nine-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al., (2017)In general, the five-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with and evaluation of wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

scholarly journals Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain

2020 ◽  
Author(s):  
Sara Top ◽  
Lola Kotova ◽  
Lesley De Cruz ◽  
Svetlana Aniskevich ◽  
Leonid Bobylev ◽  
...  
Keyword(s):  
High Resolution ◽  
Central Asia ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Horizontal Resolution ◽  
Maximum Temperature ◽  
Climate Projections ◽  
Climate Information ◽  
Climate Data

Abstract. To allow for climate impact studies on human and natural systems high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. This publication aims at addressing one of these regional gaps by presenting an evaluation study for two regional climate models (RCMs) (REMO and ALARO-0) at a horizontal resolution of 0.22° (25 km) over Central Asia. The output of the ERA-Interim driven RCMs is compared with different observational datasets over the 1980–2017 period. The choice of the observational dataset has an impact on the scores but in general one can conclude that both models reproduce reasonably well the spatial patterns for temperature and precipitation. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. More detailed studies of the annual cycle over subregions should be carried out to reveal whether this is due to an incorrect simulation in cloud cover, atmospheric circulation or heat and moisture fluxes. In general, the REMO model scores better for temperature whereas the ALARO-0 model prevails for precipitation. This publication demonstrates that the REMO and ALARO-0 RCMs can be used to perform climate projections over Central Asia and that the produced climate data can be applied in impact modelling.

scholarly journals WegenerNet high-resolution weather and climate data 2007 to 2019

2020 ◽  
Author(s):  
Jürgen Fuchsberger ◽  
Gottfried Kirchengast ◽  
Thomas Kabas
Keyword(s):  
Relative Humidity ◽  
Air Temperature ◽  
Heat Index ◽  
Spatial Density ◽  
Wind Gust ◽  
Climate Data ◽  
Near Surface ◽  
Temporal Sampling ◽  
Weather And Climate ◽  
Data Level

Abstract. This paper describes the latest reprocessed data record (version 7.1) over 2007 to 2019 from the WegenerNet climate station networks, which since 2007 provide measurements with very high spatial and temporal resolution of hydrometeorological variables for two regions in the state of Styria, southeastern Austria: 1) the WegenerNet Feldbach Region, in the Alpine forelands of southeastern Styria, which extends over an area of about 22 km × 16 km and comprises 155 meteorological stations placed on a tightly spaced grid, with an average spatial density of one station per ∼2 km2 and a temporal sampling of 5 min; and 2) the WegenerNet Johnsbachtal, which is a smaller sister network of the WegenerNet Feldbach Region in the mountainous Alpine region of upper Styria that extends over an area of about 16 km ×17 km and comprises 13 meteorological stations and one hydrographic station, at altitudes ranging from below 600 m to over 2100 m and with a temporal sampling of 10 min. These networks operate on a long-term basis and continuously provide quality-controlled station time series for a multitude of hydrometeorological near-surface and surface variables, including air temperature, relative humidity, precipitation, wind speed and direction, wind gust speed and direction, soil moisture, soil temperature, and others like pressure and radiation variables at a few reference stations. In addition, gridded data are available at a resolution of 200 m × 200 m for air temperature, relative humidity, precipitation and heat index, for the Feldbach Region, and at a resolution of 100 m × 100 m for the wind parameters for both regions. Here we describe this dataset (the most recent reprocessing version 7.1), in terms of the measurement site and station characteristics as well as the data processing from raw data (level 0) via quality-controlled basic station data (level 1) to weather and climate data products (level 2). In order to showcase the practical utility of the data we also include two illustrative example applications and briefly summarize and refer to scientific uses in a range of previous studies. The dataset is published as part of the University of Graz Wegener Center's WegenerNet data repository under the DOI https://doi.org/10.25364/WEGC/WPS7.1:2020.1 (Fuchsberger et al., 2020) and is continuously extended.

scholarly journals Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

2018 ◽  
Vol 11 (10) ◽  
pp. 5607-5627 ◽  
Author(s):  
Christoph Schlager ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger
Keyword(s):  
High Resolution ◽  
Climate Models ◽  
Meteorological Station ◽  
Strong Wind ◽  
Mountainous Region ◽  
Climate Data ◽  
Wind Fields ◽  
Station Network ◽  
Weather And Climate ◽  
Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real-time, recently developed by the authors Schlager et al. (2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains 11 meteorological stations at elevations from about 600 to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 min from the WegenerNet JBT, mean wind and wind gust fields at 10 and 50 m height levels with a high spatial resolution of 100 m × 100 m and a temporal resolution of 30 min. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. The main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance. Due to the spatially more homogeneous wind speeds and directions for strong wind events in this mountainous region, the results show somewhat better performance for these events. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from 5-year climate data of the WegenerNet JBT in comparison to 9-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al. (2017). In general, the 5-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with, and evaluation of, wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

A Southeastern South American Daily Gridded Dataset of Observed Surface Minimum and Maximum Temperature for 1961–2000

2011 ◽  
Vol 92 (10) ◽  
pp. 1339-1346 ◽  
Author(s):  
Bárbara Tencer ◽  
Matilde Rusticucci ◽  
Phil Jones ◽  
David Lister
Keyword(s):  
South America ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Maximum Temperature ◽  
Data Series ◽  
South American ◽  
Mean Values ◽  
Gridded Dataset ◽  
Mean Square Errors

This study presents a southeastern South American gridded dataset of daily minimum and maximum surface temperatures for 1961–2000. The data used for the gridding are observed daily data from meteorological stations in Argentina, Brazil, Paraguay, and Uruguay from the database of the European Community's Sixth Framework Programme A Europe–South America Network for Climate Change Assessment and Impact Studies in La Plata Basin (EU FP6 CLARIS LPB), with some additional data series. This gridded dataset is new for the region, not only for its spatial and temporal extension, but also for its temporal resolution. The region for which the gridded dataset has been developed is 20°–40°S, 45°–70°W, with a resolution of 0.5° latitude × 0.5° longitude. Since the methodology used produces an estimation of gridbox averages, the developed dataset is very useful for the validation of regional climate models. The comparison of gridded and observed data provides an evaluation of the usefulness of the interpolated data. According to monthly-mean values and daily variability, the methodology of interpolation developed during the EU FP6 ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) project for its application in Europe is also suitable for southeastern South America. Root-mean-square errors for the whole region are 1.77°C for minimum temperature and 1.13°C for maximum temperature. These errors are comparable to values obtained for Europe with the same methodology.

scholarly journals Evaluation of multiple climate data sources for managing environmental resources in East Africa

2018 ◽  
Vol 22 (8) ◽  
pp. 4547-4564 ◽  
Author(s):  
Solomon Hailu Gebrechorkos ◽  
Stephan Hülsmann ◽  
Christian Bernhofer
Keyword(s):  
East Africa ◽  
Climate Models ◽  
Regional Climate ◽  
Daily Rainfall ◽  
Grid Cell ◽  
Regional Climate Models ◽  
Data Sources ◽  
Climate Data ◽  
Station Data ◽  
Cell Average

Abstract. Managing environmental resources under conditions of climate change and extreme climate events remains among the most challenging research tasks in the field of sustainable development. A particular challenge in many regions such as East Africa is often the lack of sufficiently long-term and spatially representative observed climate data. To overcome this data challenge we used a combination of accessible data sources based on station data, earth observations by remote sensing, and regional climate models. The accuracy of the Africa Rainfall Climatology version 2.0 (ARC2), Climate Hazards Group InfraRed Precipitation (CHIRP), CHIRP with Station data (CHIRPS), Observational-Reanalysis Hybrid (ORH), and regional climate models (RCMs) are evaluated against station data obtained from the respective national weather services and international databases. We did so by performing a comparison in three ways: point to pixel, point to area grid cell average, and stations' average to area grid cell average over 21 regions of East Africa: 17 in Ethiopia, 2 in Kenya, and 2 in Tanzania. We found that the latter method provides better correlation and significantly reduces biases and errors. The correlations were analysed at daily, dekadal (10 days), and monthly resolution for rainfall and maximum and minimum temperature (Tmax and Tmin) covering the period of 1983–2005. At a daily timescale, CHIRPS, followed by ARC2 and CHIRP, is the best performing rainfall product compared to ORH, individual RCMs (I-RCM), and RCMs' mean (RCMs). CHIRPS captures the daily rainfall characteristics well, such as average daily rainfall, amount of wet periods, and total rainfall. Compared to CHIRPS, ARC2 showed higher underestimation of the total (−30 %) and daily (−14 %) rainfall. CHIRP, on the other hand, showed higher underestimation of the average daily rainfall (−53 %) and duration of dry periods (−29 %). Overall, the evaluation revealed that in terms of multiple statistical measures used on daily, dekadal, and monthly timescales, CHIRPS, CHIRP, and ARC2 are the best performing rainfall products, while ORH, I-RCM, and RCMs are the worst performing products. For Tmax and Tmin, ORH was identified as the most suitable product compared to I-RCM and RCMs. Our results indicate that CHIRPS (rainfall) and ORH (Tmax and Tmin), with higher spatial resolution, should be the preferential data sources to be used for climate change and hydrological studies in areas of East Africa where station data are not accessible.

scholarly journals Application of SWAT in Hydrological Simulation of Complex Mountainous River Basin (Part II: Climate Change Impact Assessment)

Water ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 1548
Author(s):  
Suresh Marahatta ◽  
Deepak Aryal ◽  
Laxmi Prasad Devkota ◽  
Utsav Bhattarai ◽  
Dibesh Shrestha
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Climate Models ◽  
Assessment Tool ◽  
Swat Model ◽  
Global Climate Models ◽  
Climate Data ◽  
The Future ◽  
The Impact ◽  
Mountainous River

This study aims at analysing the impact of climate change (CC) on the river hydrology of a complex mountainous river basin—the Budhigandaki River Basin (BRB)—using the Soil and Water Assessment Tool (SWAT) hydrological model that was calibrated and validated in Part I of this research. A relatively new approach of selecting global climate models (GCMs) for each of the two selected RCPs, 4.5 (stabilization scenario) and 8.5 (high emission scenario), representing four extreme cases (warm-wet, cold-wet, warm-dry, and cold-dry conditions), was applied. Future climate data was bias corrected using a quantile mapping method. The bias-corrected GCM data were forced into the SWAT model one at a time to simulate the future flows of BRB for three 30-year time windows: Immediate Future (2021–2050), Mid Future (2046–2075), and Far Future (2070–2099). The projected flows were compared with the corresponding monthly, seasonal, annual, and fractional differences of extreme flows of the simulated baseline period (1983–2012). The results showed that future long-term average annual flows are expected to increase in all climatic conditions for both RCPs compared to the baseline. The range of predicted changes in future monthly, seasonal, and annual flows shows high uncertainty. The comparative frequency analysis of the annual one-day-maximum and -minimum flows shows increased high flows and decreased low flows in the future. These results imply the necessity for design modifications in hydraulic structures as well as the preference of storage over run-of-river water resources development projects in the study basin from the perspective of climate resilience.

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