1981-2010 U. S. Hourly Normals

This is the hourly normals meteorological data

An enhanced integrated water vapour dataset from more than 10 000 global ground-based GPS stations in 2020

We developed a high-quality global integrated water vapour (IWV) dataset from 12 552 ground-based global positioning system (GPS) stations in 2020. It consists of 5 min GPS IWV estimates with a total number of 1 093 591 492 data points. The completeness rates of the IWV estimates are higher than 95 % at 7253 (58 %) stations. The dataset is an enhanced version of the existing operational GPS IWV dataset provided by the Nevada Geodetic Laboratory (NGL). The enhancement is reached by employing accurate meteorological information from the fifth generation of European ReAnalysis (ERA5) for the GPS IWV retrieval with a significantly higher spatiotemporal resolution. A dedicated data screening algorithm is also implemented. The GPS IWV dataset has a good agreement with in situ radiosonde observations at 182 collocated stations worldwide. The IWV biases are within ±3.0 kg m−2 with a mean absolute bias (MAB) value of 0.69 kg m−2. The standard deviations (SD) of IWV differences are no larger than 3.4 kg m−2. In addition, the enhanced IWV product shows substantial improvements compared to NGL\u27s operational version, and it is thus recommended for high-accuracy applications, such as research of extreme weather events and diurnal variations of IWV and intercomparisons with other IWV retrieval techniques. Taking the radiosonde-derived IWV as reference, the MAB and SD of IWV differences are reduced by 19.5 % and 6.2 % on average, respectively. The number of unrealistic negative GPS IWV estimates is also substantially reduced by 92.4 % owing to the accurate zenith hydrostatic delay (ZHD) derived by ERA5. The dataset is available at https://doi.org/10.5281/zenodo.6973528 (Yuan et al., 2022)

Anomalous mid-twentieth century atmospheric circulation change over the South Atlantic compared to the last 6000 years

Determining the timing and impact of anthropogenic climate change in data-sparse regions is a considerable challenge. Arguably, nowhere is this more difficult than the Antarctic Peninsula and the subantarctic South Atlantic where observational records are relatively short but where high rates of warming have been experienced since records began. Here we interrogate recently developed monthly-resolved observational datasets from the Falkland Islands and South Georgia, and extend the records back using climate-sensitive peat growth over the past 6000 years. Investigating the subantarctic climate data with ERA-Interim and Twentieth Century Reanalysis, we find that a stepped increase in precipitation across the 1940s is related to a change in synoptic atmospheric circulation: a westward migration of quasi-permanent positive pressure anomalies in the South Atlantic has brought the subantarctic islands under the increased influence of meridional airflow associated with the Amundsen Sea Low. Analysis of three comprehensively multi-dated (using 14C and 137Cs) peat sequences across the two islands demonstrates unprecedented growth rates since the mid-twentieth century relative to the last 6000 years. Comparison to observational and reconstructed sea surface temperatures suggests this change is linked to a warming tropical Pacific Ocean. Our results imply 'modern' South Atlantic atmospheric circulation has not been under this configuration for millennia

The International Surface Pressure Databank version 2

The International Surface Pressure Databank (ISPD) is the world's largest collection of global surface and sea-level pressure observations. It was developed by extracting observations from established international archives, through international cooperation with data recovery facilitated by the Atmospheric Circulation Reconstructions over the Earth (ACRE) initiative, and directly by contributing universities, organizations, and countries. The dataset period is currently 1768–2012 and consists of three data components: observations from land stations, marine observing systems, and tropical cyclone best track pressure reports. Version 2 of the ISPD (ISPDv2) was created to be observational input for the Twentieth Century Reanalysis Project (20CR) and contains the quality control and assimilation feedback metadata from the 20CR. Since then, it has been used for various general climate and weather studies, and an updated version 3 (ISPDv3) has been used in the ERA-20C reanalysis in connection with the European Reanalysis of Global Climate Observations project (ERA-CLIM). The focus of this paper is on the ISPDv2 and the inclusion of the 20CR feedback metadata. The Research Data Archive at the National Center for Atmospheric Research provides data collection and access for the ISPDv2, and will provide access to future versions

The state of the Martian climate

60°N was +2.0°C, relative to the 1981–2010 average value (Fig. 5.1). This marks a new high for the record. The average annual surface air temperature (SAT) anomaly for 2016 for land stations north of starting in 1900, and is a significant increase over the previous highest value of +1.2°C, which was observed in 2007, 2011, and 2015. Average global annual temperatures also showed record values in 2015 and 2016. Currently, the Arctic is warming at more than twice the rate of lower latitudes

Towards a more reliable historical reanalysis: improvements for version 3 of the Twentieth Century Reanalysis system

Historical reanalyses that span more than a century are needed for a wide range of studies, from understanding large‐scale climate trends to diagnosing the impacts of individual historical extreme weather events. The Twentieth Century Reanalysis (20CR) Project is an effort to fill this need. It is supported by the National Oceanic and Atmospheric Administration (NOAA), the Cooperative Institute for Research in Environmental Sciences (CIRES), and the U.S. Department of Energy (DOE), and is facilitated by collaboration with the international Atmospheric Circulation Reconstructions over the Earth initiative. 20CR is the first ensemble of sub‐daily global atmospheric conditions spanning over 100 years. This provides a best estimate of the weather at any given place and time as well as an estimate of its confidence and uncertainty. While extremely useful, version 2c of this dataset (20CRv2c) has several significant issues, including inaccurate estimates of confidence and a global sea level pressure bias in the mid‐19th century. These and other issues can reduce its effectiveness for studies at many spatial and temporal scales. Therefore, the 20CR system underwent a series of developments to generate a significant new version of the reanalysis. The version 3 system (NOAA‐CIRES‐DOE 20CRv3) uses upgraded data assimilation methods including an adaptive inflation algorithm; has a newer, higher‐resolution forecast model that specifies dry air mass; and assimilates a larger set of pressure observations. These changes have improved the ensemble‐based estimates of confidence, removed spin‐up effects in the precipitation fields, and diminished the sea‐level pressure bias. Other improvements include more accurate representations of storm intensity, smaller errors, and large‐scale reductions in model bias. The 20CRv3 system is comprehensively reviewed, focusing on the aspects that have ameliorated issues in 20CRv2c. Despite the many improvements, some challenges remain, including a systematic bias in tropical precipitation and time‐varying biases in southern high‐latitude pressure fields

On distinguishing snowfall from rainfall using near-surface atmospheric information: Comparative analysis, uncertainties and hydrologic importance

The accurate estimation of precipitation phase has broad applications. In this study, we compared the skill of using various atmospheric variables and their combinations as predictors in accurately identifying surface precipitation phase, determined uncertainties associated with commonly used fixed temperature thresholds, and explored the sensitivity of hydrologic model output to uncertainty in precipitation phase using two case-studies. The results suggest that among all single predictors, wet-bulb temperature yields the highest skill score for determining precipitation phase and can reduce uncertainties due to regional differences, especially compared to the commonly used near-surface air temperature. However, addition of good-quality near-surface wind speed measurement to dew-point temperature and pressure showed slightly higher skill than wet-bulb temperature. We showed that the scale mismatch between temperature from stations and gridded products can cause large uncertainties in determining precipitation phase, especially in regions with rugged topography. Such uncertainties need to be considered when the relationships developed based on station data are applied to remote-sensing observations and model-generated data to separate rain from snowfall. The sensitivity of hydrologic model outputs to uncertainty in precipitation phase delineation was also assessed over two major basins in California by modifying default near-surface temperatures used in the Variable Infiltration Capacity (VIC) model. It was found that regional and scaling uncertainties in determining temperature thresholds can largely influence the accuracy of simulated downstream runoff and snow water equivalent (SWE) (e.g. up to 40% change in SWE for 2 degrees C shift in temperature threshold). Therefore, to reduce simulation uncertainties, it is important to improve rain-snow partitioning methods, consider regional variabilities in determining temperature thresholds, and perform the analysis at the highest possible resolutions to mitigate scale-related uncertainties.National Aeronautics and Space Administration (NASA) GRACE [NNH15ZDA001N-GRACE]; NASA Energy and Water Cycle Study (NEWS) [NNH13ZDA001N-NEWS]; US Department of Agriculture/National Institute of Food Agriculture; National Science Foundation; Water Sustainability & Climate Program [1360506/1360507]; NASA Weather [NNH13ZDA001N-WEATHER]; NASA MIRO [NNX15AQ06A]12 month embargo; published on: 17 August 2018This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]