scholarly journals Revisiting the Global Seasonal Snow Classification: An Updated Dataset for Earth System Applications

2021 ◽  
Author(s):  
Matthew Sturm ◽  
Glen E. Liston
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Air Temperature ◽  
Classification System ◽  
European Space Agency ◽  
Physical Geography ◽  
Meteorological Model ◽  
Seasonal Snow ◽  
Temperature And Precipitation ◽  
Global Datasets

AbstractTwenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land-cover. When introduced in 1995, the finest resolution global datasets of these variables were on a 0.5° × 0.5° latitude-longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arcsecond × 10-arcsecond latitude-longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude-longitude (approximately 10 km) gridded meteorological climatologies (1981-2019, European Centre for Medium-Range Weather Forecasts [ECMWF] ReAnalysis, 5th Generation Land [ERA5-Land]) using MicroMet, a spatially distributed, high-resolution, micro-meteorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification 2.0) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

scholarly journals Evaluation of High-Resolution Crop Model Meteorological Forcing Datasets at Regional Scale: Air Temperature and Precipitation over Major Land Areas of China

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 1011
Author(s):  
Qiuling Wang ◽  
Wei Li ◽  
Chan Xiao ◽  
Wanxiu Ai
Keyword(s):  
High Temperature ◽  
High Resolution ◽  
Air Temperature ◽  
Regional Scale ◽  
Hydrological Process ◽  
China Meteorological Administration ◽  
Meteorological Forcing ◽  
Factors Affecting ◽  
Temperature And Precipitation ◽  
Meteorological Modeling

Air temperature and precipitation are two important meteorological factors affecting the earth’s energy exchange and hydrological process. High quality temperature and precipitation forcing datasets are of great significance to agro-meteorology and disaster monitoring. In this study, the accuracy of air temperature and precipitation of the fifth generation of atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ERA5) and High-Resolution China Meteorological Administration Land Data Assimilation System (HRCLDAS) datasets are compared and evaluated from multiple spatial–temporal perspectives based on the ground meteorological station observations over major land areas of China in 2018. Concurrently, the applicability to the monitoring of high temperatures and rainstorms is also distinguished. The results show that (1) although both forcing datasets can capture the broad features of spatial distribution and seasonal variation in air temperature and precipitation, HRCLDAS shows more detailed features, especially in areas with complex underlying surfaces; (2) compared with the ground observations, it can be found that the air temperature and precipitation of HRCLDAS perform better than ERA5. The root-mean-square error (RMSE) of mean air temperature are 1.3 °C for HRCLDAS and 2.3 °C for ERA5, and the RMSE of precipitation are 2.4 mm for HRCLDAS and 5.4 mm for ERA5; (3) in the monitoring of important weather processes, the two forcing datasets can well reproduce the high temperature, rainstorm and heavy rainstorm events from June to August in 2018. HRCLDAS is more accurate in the area and magnitude of high temperature and rainstorm due to its high spatial and temporal resolution. The evaluation results can help researchers to understand the superiority and drawbacks of these two forcing datasets and select datasets reasonably in the study of climate change, agro-meteorological modeling, extreme weather research, hydrological processes and sustainable development.

scholarly journals High resolution biogenic global emission inventory for the time period 2000–2019 for air quality modelling

2021 ◽  
Author(s):  
Katerina Sindelarova ◽  
Jana Markova ◽  
David Simpson ◽  
Peter Huszar ◽  
Jan Karlicky ◽  
...  
Keyword(s):  
Air Quality ◽  
High Resolution ◽  
Land Cover ◽  
European Space Agency ◽  
Atmospheric Composition ◽  
Terrestrial Vegetation ◽  
Ancillary Data ◽  
Temporal And Spatial Distribution ◽  
Uncertainty Range ◽  
Global Emission

Abstract. Biogenic volatile organic compounds (BVOCs) emitted from the terrestrial vegetation into the Earth’s atmosphere play an important role in atmospheric chemical processes. A gridded information of their temporal and spatial distribution is therefore needed for proper representation of the atmospheric composition by the air quality models. Here we present three newly developed high-resolution global emission inventories of the main BVOC species including isoprene, monoterpenes, sesquiterpenes, methanol, acetone and ethene. Monthly mean and monthly averaged daily profile emissions were calculated by the Model of Emission of Gases and Aerosols from Nature (MEGANv2.1) driven by meteorological reanalyzes of the European Centre for Medium-Range Weather Forecasts for the period of 2000–2019. The dataset CAMS-GLOB-BIOv1.2 is based on ERA-Interim meteorology, datasets CAMS-GLOB-BIOv3.0 and v3.1 were calculated with ERA5. Furthermore, European isoprene emission potential data were updated using high-resolution land cover maps and detailed information of tree species composition and emission factors from the EMEP MSC-W model system. Updated isoprene emissions are included in CAMS-GLOB-BIOv3.1 dataset. The effect of annually changing land cover on BVOC emissions is captured by the CAMS-GLOB-BIOv3.0 as it was calculated with land cover data provided by the Climate Change Initiative of the European Space Agency (ESA-CCI). The global total annual BVOC emissions averaged over the simulated period vary between the datasets from 424 to 591 Tg(C) yr−1, with isoprene emissions from 299.1 to 440.5 Tg(isoprene) yr−1. Differences between the datasets and variation in their emission estimates suggests the emission uncertainty range and the main sources of uncertainty, i.e. meteorological inputs, emission potential data and land cover description. The CAMS-GLOB-BIO time series of isoprene and monoterpenes were compared to other available data. There is a general agreement in an inter-annual variability of the emission estimates and the values fall within the uncertainty range. The CAMS-GLOB-BIO datasets (CAMS-GLOB-BIOv1.2, https://doi.org/10.24380/t53a-qw03, Sindelarova et al., 2021a; CAMS-GLOB-BIOv3.0, https://doi.org/10.24380/xs64-gj42, Sindelarova et al., 2021b; CAMS-GLOB-BIOv3.1, https://doi.org/10.24380/cv4p-5f79, Sindelarova et al., 2021c) are distributed from the Emissions of atmospheric Compounds and Compilation of Ancillary Data (ECCAD) system (https://eccad.aeris-data.fr/, last access: June 2021).

scholarly journals Combining High-Resolution Land Use Data With Crowdsourced Air Temperature to Investigate Intra-Urban Microclimate

2021 ◽  
Vol 9 ◽  
Author(s):  
Julia Potgieter ◽  
Negin Nazarian ◽  
Mathew J. Lipson ◽  
Melissa A. Hart ◽  
Giulia Ulpiani ◽  
...  
Keyword(s):  
Land Use ◽  
Sensor Networks ◽  
High Resolution ◽  
Land Cover ◽  
Spatial Variability ◽  
Air Temperature ◽  
Urban Land Use ◽  
Crowdsourced Data ◽  
Weather Stations ◽  
Maximum Temperatures

The spatial variability of land cover in cities results in a heterogeneous urban microclimate, which is often not represented with regulatory meteorological sensor networks. Crowdsourced sensor networks have the potential to address this shortcoming with real-time and fine-grained temperature measurements across cities. We use crowdsourced data from over 500 citizen weather stations during summer in Sydney, Australia, combined with 100-m land use and Local Climate Zone (LCZ) maps to explore intra-urban variabilities in air temperature. Sydney presents unique drivers for spatio-temporal variability, with its climate influenced by the ocean, mountainous topography, and diverse urban land use. Here, we explore the interplay of geography with urban form and fabric on spatial variability in urban temperatures. The crowdsourced data consists of 2.3 million data points that were quality controlled and compared with reference data from five synoptic weather stations. Crowdsourced stations measured higher night-time temperatures, higher maximum temperatures on warm days, and cooler maximum temperatures on cool days compared to the reference stations. These differences are likely due to siting, with crowdsourced weather stations closer to anthropogenic heat emissions, urban materials with high thermal inertia, and in areas of reduced sky view factor. Distance from the coast was found to be the dominant factor impacting the spatial variability in urban temperatures, with diurnal temperature range greater for sensors located inland. Further differences in urban temperature could be explained by spatial variability in urban land-use and land-cover. Temperature varied both within and between LCZs across the city. Crowdsourced nocturnal temperatures were particularly sensitive to surrounding land cover, with lower temperatures in regions with higher vegetation cover, and higher temperatures in regions with more impervious surfaces. Crowdsourced weather stations provide highly relevant data for health monitoring and urban planning, however, there are several challenges to overcome to interpret this data including a lack of metadata and an uneven distribution of stations with a possible socio-economic bias. The sheer number of crowdsourced weather stations available can provide a high-resolution understanding of the variability of urban heat that is not possible to obtain via traditional networks.

scholarly journals Improved Seasonal Prediction of Temperature and Precipitation over Land in a High-Resolution GFDL Climate Model

2015 ◽  
Vol 28 (5) ◽  
pp. 2044-2062 ◽  
Author(s):  
Liwei Jia ◽  
Xiaosong Yang ◽  
Gabriel A. Vecchi ◽  
Richard G. Gudgel ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
High Resolution ◽  
Air Temperature ◽  
Climate Model ◽  
Seasonal Prediction ◽  
Boreal Winter ◽  
Optimization Approach ◽  
Temperature And Precipitation ◽  
Model Predictions ◽  
Resolution Model ◽  
High Resolution Model

Abstract This study demonstrates skillful seasonal prediction of 2-m air temperature and precipitation over land in a new high-resolution climate model developed by the Geophysical Fluid Dynamics Laboratory and explores the possible sources of the skill. The authors employ a statistical optimization approach to identify the most predictable components of seasonal mean temperature and precipitation over land and demonstrate the predictive skill of these components. First, the improved skill of the high-resolution model over the previous lower-resolution model in seasonal prediction of the Niño-3.4 index and other aspects of interest is shown. Then, the skill of temperature and precipitation in the high-resolution model for boreal winter and summer is measured, and the sources of the skill are diagnosed. Last, predictions are reconstructed using a few of the most predictable components to yield more skillful predictions than the raw model predictions. Over three decades of hindcasts, the two most predictable components of temperature are characterized by a component that is likely due to changes in external radiative forcing in boreal winter and summer and an ENSO-related pattern in boreal winter. The most predictable components of precipitation in both seasons are very likely ENSO-related. These components of temperature and precipitation can be predicted with significant correlation skill at least 9 months in advance. The reconstructed predictions using only the first few predictable components from the model show considerably better skill relative to observations than raw model predictions. This study shows that the use of refined statistical analysis and a high-resolution dynamical model leads to significant skill in seasonal predictions of 2-m air temperature and precipitation over land.

scholarly journals Assessment of High Resolution Air Temperature Fields at Rocky Mountain National Park by Combining Scarce Point Measurements with Elevation and Remote Sensing Data

2020 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Antonio-Juan Collados-Lara ◽  
Steven R. Fassnacht ◽  
Eulogio Pardo-Igúzquiza ◽  
David Pulido-Velazquez
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Air Temperature ◽  
Land Surface ◽  
National Park ◽  
Rocky Mountain ◽  
Rocky Mountain National Park ◽  
Temperature Fields ◽  
Lapse Rate ◽  
Validation Experiment

There is necessity of considering air temperature to simulate the hydrology and management within water resources systems. In many cases, a big issue is considering the scarcity of data due to poor accessibility and limited funds. This paper proposes a methodology to obtain high resolution air temperature fields by combining scarce point measurements with elevation data and land surface temperature (LST) data from remote sensing. The available station data (SNOTEL stations) are sparse at Rocky Mountain National Park, necessitating the inclusion of correlated and well-sampled variables to assess the spatial variability of air temperature. Different geostatistical approaches and weighted solutions thereof were employed to obtain air temperature fields. These estimates were compared with two relatively direct solutions, the LST (MODIS) and a lapse rate-based interpolation technique. The methodology was evaluated using data from different seasons. The performance of the techniques was assessed through a cross validation experiment. In both cases, the weighted kriging with external drift solution (considering LST and elevation) showed the best results, with a mean squared error of 3.7 and 3.6 °C2 for the application and validation, respectively.

scholarly journals Quantifying the Independent Influences of Land Cover and Humidity on Microscale Urban Air Temperature Variation in Hot Summer: Methods of Path Analysis and Genetic SVR

Atmosphere ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1377
Author(s):  
Weifang Shi ◽  
Nan Wang ◽  
Aixuan Xin ◽  
Linglan Liu ◽  
Jiaqi Hou ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Land Cover ◽  
Path Analysis ◽  
Temperature Variation ◽  
Direct Effect ◽  
Air Temperature ◽  
Urban Areas ◽  
Support Vector ◽  
Urban Air ◽  
Air Temperatures

Mitigating high air temperatures and heat waves is vital for decreasing air pollution and protecting public health. To improve understanding of microscale urban air temperature variation, this paper performed measurements of air temperature and relative humidity in a field of Wuhan City in the afternoon of hot summer days, and used path analysis and genetic support vector regression (SVR) to quantify the independent influences of land cover and humidity on air temperature variation. The path analysis shows that most effect of the land cover is mediated through relative humidity difference, more than four times as much as the direct effect, and that the direct effect of relative humidity difference is nearly six times that of land cover, even larger than the total effect of the land cover. The SVR simulation illustrates that land cover and relative humidity independently contribute 16.3% and 83.7%, on average, to the rise of the air temperature over the land without vegetation in the study site. An alternative strategy of increasing the humidity artificially is proposed to reduce high air temperatures in urban areas. The study would provide scientific support for the regulation of the microclimate and the mitigation of the high air temperature in urban areas.

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