Mining Weather and Climate Data from the Diary of a Forty-Niner

2020 ◽  
pp. 149-162
Author(s):  
Jase Bernhardt
Keyword(s):  
Climate Data ◽  
Weather And Climate

Solar Signals in Surface Climate

2015 ◽  
Author(s):  
Joanna D. Haigh ◽  
Peter Cargill
Keyword(s):  
Climate Change ◽  
The Sun ◽  
Climate Data ◽  
Growth Rings ◽  
Surface Climate ◽  
Annual Growth Rings ◽  
Weather And Climate ◽  
Long Time ◽  
Established Technique

This chapter looks at how the Sun varies in terms of its emissions of radiation and particles and how these changes might be associated with variations in weather and climate on Earth. Investigations of climate variability and climate change depend crucially on the existence, length, and quality of meteorological records. Ideally, records would consist of long time series of measurements made by well-calibrated instruments densely situated across the globe. For longer periods, and in remote regions, records have to be reconstructed from indirect indicators of climate known as proxy data. The chapter introduces one well-established technique for providing proxy climate data: dendrochronology, or the study of the successive annual growth rings of trees.

scholarly journals The Risks of Contracting the Acquisition and Processing of the Nation’s Weather and Climate Data to the Private Sector

2018 ◽  
Vol 99 (5) ◽  
pp. 869-870 ◽  
Author(s):  
Yolande L. Serra ◽  
Jennifer S. Haase ◽  
David K. Adams ◽  
Qiang Fu ◽  
Thomas P. Ackerman ◽  
...  
Keyword(s):  
Private Sector ◽  
Climate Data ◽  
Weather And Climate

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.

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 Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

2021 ◽  
Vol 3 ◽  
Author(s):  
Agon Serifi ◽  
Tobias Günther ◽  
Nikolina Ban
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Network Architecture ◽  
Supervised Machine Learning ◽  
Learning Performance ◽  
Data Reconstruction ◽  
Practical Reasons ◽  
Climate Data ◽  
Low Resolution ◽  
Weather And Climate

Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume continues to increase rapidly since an increase in resolution greatly benefits the simulation of weather and climate. In practice, however, data is often available at lower resolution only, for which there are many practical reasons, such as data coarsening to meet memory constraints, limited computational resources, favoring multiple low-resolution ensemble simulations over few high-resolution simulations, as well as limits of sensing instruments in observations. In order to enable a more insightful analysis, we investigate the capabilities of neural networks to reconstruct high-resolution data from given low-resolution simulations. For this, we phrase the data reconstruction as a super-resolution problem from multiple data sources, tailored toward meteorological and climatological data. We therefore investigate supervised machine learning using multiple deep convolutional neural network architectures to test the limits of data reconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalization to numerical and observed data. Once such downscaling networks are trained, they serve two purposes: First, legacy low-resolution simulations can be downscaled to reconstruct high-resolution detail. Second, past observations that have been taken at lower resolutions can be increased to higher resolutions, opening new analysis possibilities. For the downscaling of high-frequent fields like precipitation, we show that error-predicting networks are far less suitable than deconvolutional neural networks due to the poor learning performance. We demonstrate that deep convolutional downscaling has the potential to become a building block of modern weather and climate analysis in both research and operational forecasting, and show that the ideal choice of the network architecture depends on the type of data to predict, i.e., there is no single best architecture for all variables.

scholarly journals Extracting weather information from a plantation document

2019 ◽  
Vol 15 (2) ◽  
pp. 477-492
Author(s):  
Gregory Burris ◽  
Jane Washburn ◽  
Omar Lasheen ◽  
Sophia Dorribo ◽  
James B. Elsner ◽  
...  
Keyword(s):  
Historical Period ◽  
Climate Data ◽  
Temperature Index ◽  
Weather Information ◽  
Historical Archives ◽  
Weather Observations ◽  
Weather And Climate ◽  
Climate Studies ◽  
Modern Era ◽  
Local Weather

Abstract. The authors introduce a method for extracting weather and climate data from a historical plantation document. They demonstrate the method on a document from Shirley Plantation in Virginia (USA) covering the period 1816–1842. They show how the resulting data are organized into a spreadsheet that includes direct weather observations and information on various cultivars. They then give three examples showing how the data can be used for climate studies. The first example is a comparison of spring onset between the plantation era and the modern era. A modern median final spring freeze event (for the years 1943–2017) occurs a week earlier than the historical median (for the years 1822–1839). The second analysis involves developing an index for midsummer temperatures from the timing of the first malaria-like symptoms in the plantation population each year. The median day when these symptoms would begin occurring in the modern period is a month and a half earlier than the median day they occurred in the historical period. The final example is a three-point temperature index generated from ordinal weather descriptions in the document. The authors suggest that this type of local weather information from historical archives, either direct from observations or indirect from phenophase timing, can be useful toward a more complete understanding of climates of the past.

scholarly journals Assimilating Weather Data into a Digital Event Gazetteer of Airborne Parachute Operations during the French Indochina War

2017 ◽  
Vol 10 (1) ◽  
pp. 19-34
Author(s):  
Johnathan P. Kirk ◽  
Gordon A. Cromley
Keyword(s):  
Two Dimensions ◽  
Weather Data ◽  
Climate Data ◽  
Historical Events ◽  
Reanalysis Dataset ◽  
Climate Conditions ◽  
Synoptic Classification ◽  
Weather Patterns ◽  
Weather And Climate ◽  
Indochina War

Abstract Modern datasets cataloging historical events, known as digital event gazetteers, feature spatiotemporal data regarding events that enable analysis through parameters including location and other descriptive information of those events. Weather and climate data represent two dimensions of spatiotemporal information, which can enhance understanding of historical events. A recently published digital event gazetteer of airborne parachute operations [opérations aéroportées (OAPs)] during and prior to the French Indochina War, spanning from 1945 to 1954, represents an opportunity to associate discrete historical events with weather information. This study outlines a methodology for assimilating weather data into the construct of a digital event gazetteer and then demonstrates example analyses of how the weather and climate conditions in Indochina may relate to OAPs during the war. A synoptic classification, utilizing the self-organizing maps procedure, is performed using daily mean sea level pressure data from 1945 to 2010, from a twentieth-century reanalysis dataset, to characterize weather patterns over the Indochina Peninsula. Since observations are sparse during the years of the conflict, the resulting weather patterns are associated with modern precipitation observations in the area, as a representation of wet and dry patterns during the war. The appropriate daily weather pattern is then assigned to each OAP in order to investigate its relationship with the weather and climate patterns of Indochina, including the influence of monsoon seasons, and how the resulting precipitation patterns affected combat operations across the theater. Additionally, specific OAPs of various missions are analyzed to investigate how weather patterns may have affected operation planning during the French Indochina War.

RNCEP: global weather and climate data at your fingertips

2011 ◽  
Vol 3 (1) ◽  
pp. 65-70 ◽  
Author(s):  
Michael U. Kemp ◽  
E. Emiel van Loon ◽  
Judy Shamoun-Baranes ◽  
Willem Bouten
Keyword(s):  
Climate Data ◽  
Weather And Climate

scholarly journals The Economic Impacts of Climate Change: Evidence from Agricultural Output and Random Fluctuations in Weather: Comment

2012 ◽  
Vol 102 (7) ◽  
pp. 3749-3760 ◽  
Author(s):  
Anthony C Fisher ◽  
W. Michael Hanemann ◽  
Michael J Roberts ◽  
Wolfram Schlenker
Keyword(s):  
Climate Change ◽  
Climate Data ◽  
Weather Variables ◽  
Climate Change Projections ◽  
Assessment Report ◽  
Fourth Assessment Report ◽  
Weather And Climate ◽  
Potential Impact ◽  
Random Fluctuations ◽  
Impacts Of Climate Change

In a series of studies employing a variety of approaches, we have found that the potential impact of climate change on US agriculture is likely negative. Deschênes and Greenstone (2007) report dramatically different results based on regressions of agricultural profits and yields on weather variables. The divergence is explained by (1) missing and incorrect weather and climate data in their study; (2) their use of older climate change projections rather than the more recent and less optimistic projections from the Fourth Assessment Report; and (3) difficulties in their profit measure due to the confounding effects of storage.

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