Mapping near-surface air temperature, pressure, relative humidity and wind speed over Mainland China with high spatiotemporal resolution

2014 ◽  
Vol 31 (5) ◽  
pp. 1127-1135 ◽  
Author(s):  
Tao Li ◽  
Xiaogu Zheng ◽  
Yongjiu Dai ◽  
Chi Yang ◽  
Zhuoqi Chen ◽  
...  
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Spatiotemporal Resolution ◽  
Near Surface ◽  
High Spatiotemporal Resolution

scholarly journals Outdoor thermal performance simulation in campus area during the dry season, Yogyakarta

2019 ◽  
Vol 2 (1) ◽  
pp. 15
Author(s):  
Hasti Widyasamratri ◽  
Arif Kusumawanto ◽  
Fadhilla Tri Nugrahaini
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Thermal Performance ◽  
Meteorological Parameters ◽  
Anthropogenic Activities ◽  
Surface Air Temperature ◽  
Dry Season ◽  
Performance Simulation ◽  
Near Surface ◽  
Significant Area

The outdoor thermal performance reflects the microclimate condition in any significant area. This study simulated the thermal performance with measured and modeled three meteorological parameters, air temperature (Ta), relative humidity (RH), and wind speed in the dry season tropical city. The research focused on thermal performance simulation and distribution, here, we were neglecting anthropogenic activities as the heat source. The result showed that there were different ranges between a measured and simulated value of Ta, RH, and wind speed. The highest Ta difference between measure and simulation occurred at 11 AM, which was 1.97⸰C. The highest difference of RH occurred at 13 PM (26.75%), and the highest different of wind speed was at 11 AM (0.37 m/s) respectively. The heat distribution in the focus area was influenced by the solar direction which impacted the ground and near-surface air temperature.  

scholarly journals Dynamical Downscaling of Surface Air Temperature and Wind Field Variabilities over the Southeastern Levantine Basin, Mediterranean Sea

Climate ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 150
Author(s):  
Mohamed ElBessa ◽  
Saad Mesbah Abdelrahman ◽  
Kareem Tonbol ◽  
Mohamed Shaltout
Keyword(s):  
Wind Speed ◽  
Air Temperature ◽  
Wind Field ◽  
Climate Model ◽  
Dynamical Downscaling ◽  
Surface Wind ◽  
Surface Air Temperature ◽  
Warming Trend ◽  
Climate Data ◽  
Near Surface

The characteristics of near surface air temperature and wind field over the Southeastern Levantine (SEL) sub-basin during the period 1979–2018 were simulated. The simulation was carried out using a dynamical downscaling approach, which requires running a regional climate model system (RegCM-SVN6994) on the study domain, using lower-resolution climate data (i.e., the fifth generation of ECMWF atmospheric reanalysis of the global climate ERA5 datasets) as boundary conditions. The quality of the RegCM-SVN simulation was first verified by comparing its simulations with ERA5 for the studied region from 1979 to 2018, and then with the available five WMO weather stations from 2007 to 2018. The dynamical downscaling results proved that RegCM-SVN in its current configuration successfully simulated the observed surface air temperature and wind field. Moreover, RegCM-SVN was proved to provide similar or even better accuracy (during extreme events) than ERA5 in simulating both surface air temperature and wind speed. The simulated annual mean T2m by RegCM-SVN (from 1979 to 2018) was 20.9 °C, with a positive warming trend of 0.44 °C/decade over the study area. Moreover, the annual mean wind speed by RegCM-SVN was 4.17 m/s, demonstrating an annual negative trend of wind speed over 92% of the study area. Surface air temperatures over SEL mostly occurred within the range of 4–31 °C; however, surface wind speed rarely exceeded 10 m/s. During the study period, the seasonal features of T2m showed a general warming trend along the four seasons and showed a wind speed decreasing trend during spring and summer. The results of the RegCM-SVN simulation constitute useful information that could be utilized to fully describe the study area in terms of other atmospheric parameters.

scholarly journals Near-surface air temperature lapse rates in the mainland China during 1962-2011

2013 ◽  
Vol 118 (14) ◽  
pp. 7505-7515 ◽  
Author(s):  
Xiuping Li ◽  
Lei Wang ◽  
Deliang Chen ◽  
Kun Yang ◽  
Baolin Xue ◽  
...  
Keyword(s):  
Air Temperature ◽  
Mainland China ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Lapse Rates

scholarly journals Observing Local-Scale Variability of Near-Surface Temperature and Humidity Using a Wireless Sensor Network

2012 ◽  
Vol 51 (1) ◽  
pp. 30-41 ◽  
Author(s):  
Katharina Lengfeld ◽  
Felix Ament
Keyword(s):  
Wireless Sensor Network ◽  
Wind Speed ◽  
Surface Temperature ◽  
Sensor Network ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Specific Humidity ◽  
Wireless Sensor ◽  
Near Surface ◽  
Single Station

AbstractIn this paper the influence of surface type, wind speed, and other environmental conditions on near-surface air temperature, specific humidity, and surface temperature is studied. A wireless sensor network consisting of 13 low-cost meteorological stations was set up as a 2.3-km-long double transect in western Germany during the Fluxes and Patterns in the Soil–Vegetation–Atmosphere Scheme (FLUXPAT2009) campaign. This deployment covered various surface types, including a small river. It was found that the air temperature was mainly influenced by the distance to the river and that its variability is controlled by the wind speed. During the night, a pool of cold air formed in the valley close to the water. The specific humidity is also governed by proximity to the river, especially during the night and for low wind speeds. In contrast, the differences in surface temperature were caused by different land cover. These results can be confirmed by a cluster analysis. Setting up 13 stations in a relatively small area is not always feasible. In this study, an estimation of the error that is made by considering the effect of a reduced number of stations is given. Use of only a single station results in an error of 0.86 K in air temperature, 0.67 g kg−1 in specific humidity, and 1.4 K in surface temperature.

scholarly journals Evaluation and Comparison of Noah and Pleim–Xiu Land Surface Models in MM5 Using GÖTE2001 Data: Spatial and Temporal Variations in Near-Surface Air Temperature

2007 ◽  
Vol 46 (10) ◽  
pp. 1587-1605 ◽  
Author(s):  
J-F. Miao ◽  
D. Chen ◽  
K. Borne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Land Surface ◽  
Model Performance ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Surface Models ◽  
Urban Heat ◽  
Soil Moisture Initialization ◽  
Evident Difference

Abstract In this study, the performance of two advanced land surface models (LSMs; Noah LSM and Pleim–Xiu LSM) coupled with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5), version 3.7.2, in simulating the near-surface air temperature in the greater Göteborg area in Sweden is evaluated and compared using the GÖTE2001 field campaign data. Further, the effects of different planetary boundary layer schemes [Eta and Medium-Range Forecast (MRF) PBLs] for Noah LSM and soil moisture initialization approaches for Pleim–Xiu LSM are investigated. The investigation focuses on the evaluation and comparison of diurnal cycle intensity and maximum and minimum temperatures, as well as the urban heat island during the daytime and nighttime under the clear-sky and cloudy/rainy weather conditions for different experimental schemes. The results indicate that 1) there is an evident difference between Noah LSM and Pleim–Xiu LSM in simulating the near-surface air temperature, especially in the modeled urban heat island; 2) there is no evident difference in the model performance between the Eta PBL and MRF PBL coupled with the Noah LSM; and 3) soil moisture initialization is of crucial importance for model performance in the Pleim–Xiu LSM. In addition, owing to the recent release of MM5, version 3.7.3, some experiments done with version 3.7.2 were repeated to reveal the effects of the modifications in the Noah LSM and Pleim–Xiu LSM. The modification to longwave radiation parameterizations in Noah LSM significantly improves model performance while the adjustment of emissivity, one of the vegetation properties, affects Pleim–Xiu LSM performance to a larger extent. The study suggests that improvements both in Noah LSM physics and in Pleim–Xiu LSM initialization of soil moisture and parameterization of vegetation properties are important.

scholarly journals Evaluation of the AMPS Boundary Layer Simulations on the Ross Ice Shelf, Antarctica, with Unmanned Aircraft Observations

2017 ◽  
Vol 56 (8) ◽  
pp. 2239-2258 ◽  
Author(s):  
Jonathan D. Wille ◽  
David H. Bromwich ◽  
John J. Cassano ◽  
Melissa A. Nigro ◽  
Marian E. Mateling ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Relative Humidity ◽  
High Wind ◽  
Ice Shelf ◽  
High Wind Speed ◽  
Near Surface ◽  
Ross Ice Shelf ◽  
Low Cloud ◽  
The Antarctic

AbstractAccurately predicting moisture and stability in the Antarctic planetary boundary layer (PBL) is essential for low-cloud forecasts, especially when Antarctic forecasters often use relative humidity as a proxy for cloud cover. These forecasters typically rely on the Antarctic Mesoscale Prediction System (AMPS) Polar Weather Research and Forecasting (Polar WRF) Model for high-resolution forecasts. To complement the PBL observations from the 30-m Alexander Tall Tower! (ATT) on the Ross Ice Shelf as discussed in a recent paper by Wille and coworkers, a field campaign was conducted at the ATT site from 13 to 26 January 2014 using Small Unmanned Meteorological Observer (SUMO) aerial systems to collect PBL data. The 3-km-resolution AMPS forecast output is combined with the global European Centre for Medium-Range Weather Forecasts interim reanalysis (ERAI), SUMO flights, and ATT data to describe atmospheric conditions on the Ross Ice Shelf. The SUMO comparison showed that AMPS had an average 2–3 m s−1 high wind speed bias from the near surface to 600 m, which led to excessive mechanical mixing and reduced stability in the PBL. As discussed in previous Polar WRF studies, the Mellor–Yamada–Janjić PBL scheme is likely responsible for the high wind speed bias. The SUMO comparison also showed a near-surface 10–15-percentage-point dry relative humidity bias in AMPS that increased to a 25–30-percentage-point deficit from 200 to 400 m above the surface. A large dry bias at these critical heights for aircraft operations implies poor AMPS low-cloud forecasts. The ERAI showed that the katabatic flow from the Transantarctic Mountains is unrealistically dry in AMPS.

scholarly journals Nature-based cooling potential: a multi-type green infrastructure evaluation in Toronto, Ontario, Canada

2021 ◽  
Author(s):  
Vidya Anderson ◽  
William A. Gough
Keyword(s):  
Urban Agriculture ◽  
Air Temperature ◽  
Green Infrastructure ◽  
Urban Climate ◽  
Surface Air Temperature ◽  
Green Roofs ◽  
Near Surface ◽  
Green Walls ◽  
The Impact ◽  
Agriculture Systems

AbstractThe application of green infrastructure presents an opportunity to mitigate rising temperatures using a multi-faceted ecosystems-based approach. A controlled field study in Toronto, Ontario, Canada, evaluates the impact of nature-based solutions on near surface air temperature regulation focusing on different applications of green infrastructure. A field campaign was undertaken over the course of two summers to measure the impact of green roofs, green walls, urban vegetation and forestry systems, and urban agriculture systems on near surface air temperature. This study demonstrates that multiple types of green infrastructure applications are beneficial in regulating near surface air temperature and are not limited to specific treatments. Widespread usage of green infrastructure could be a viable strategy to cool cities and improve urban climate.

Evaluating temperature extremes in CMIP6 simulations using statistically proper evaluation methods

2021 ◽  
Author(s):  
Thordis Thorarinsdottir ◽  
Jana Sillmann ◽  
Marion Haugen ◽  
Nadine Gissibl ◽  
Marit Sandstad
Keyword(s):  
Air Temperature ◽  
Model Evaluation ◽  
Climate Models ◽  
Climate Model ◽  
Mean Squared Error ◽  
Surface Air Temperature ◽  
Cmip5 Models ◽  
Model Output ◽  
Intergovernmental Panel ◽  
Near Surface

<p>Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important as global warming is leading to significant increases in the hottest days and decreases in coldest nights around the world with considerable impacts on various sectors, such as agriculture, health and tourism.</p><p>Climate model evaluation has traditionally been performed by comparing summary statistics that are derived from simulated model output and corresponding observed quantities using, for instance, the root mean squared error (RMSE) or mean bias as also used in the model evaluation chapter of the fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5). Both RMSE and mean bias compare averages over time and/or space, ignoring the variability, or the uncertainty, in the underlying values. Particularly when interested in the evaluation of climate extremes, climate models should be evaluated by comparing the probability distribution of model output to the corresponding distribution of observed data.</p><p>To address this shortcoming, we use the integrated quadratic distance (IQD) to compare distributions of simulated indices to the corresponding distributions from a data product. The IQD is the proper divergence associated with the proper continuous ranked probability score (CRPS) as it fulfills essential decision-theoretic properties for ranking competing models and testing equality in performance, while also assessing the full distribution.</p><p>The IQD is applied to evaluate CMIP5 and CMIP6 simulations of monthly maximum (TXx) and minimum near-surface air temperature (TNn) over the data-dense regions Europe and North America against both observational and reanalysis datasets. There is not a notable difference between the model generations CMIP5 and CMIP6 when the model simulations are compared against the observational dataset HadEX2. However, the CMIP6 models show a better agreement with the reanalysis ERA5 than CMIP5 models, with a few exceptions. Overall, the climate models show higher skill when compared against ERA5 than when compared against HadEX2. While the model rankings vary with region, season and index, the model evaluation is robust against changes in the grid resolution considered in the analysis.</p>

Empirical Equations to Estimate Evapotranspiration in Mosul City

2020 ◽  
Vol 27 (4) ◽  
pp. 98-102
Author(s):  
Haqqi Yasin ◽  
Luma Abdullah
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Air Temperature ◽  
Reference Evapotranspiration ◽  
Sunshine Duration ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
Daily Data ◽  
Daily Sunshine ◽  
Measured Wind Speed

Average daily data of solar radiation, relative humidity, wind speed and air temperature from 1980 to 2008 are used to estimate the daily reference evapotranspiration in the Mosul City, North of Iraq. ETo calculator software with the Penman Monteith method standardized by the Food and Agriculture Organization is used for calculations. Further, a nonlinear regression approach using SPSS Statistics is utilized to drive the daily reference evapotranspiration relationships in which ETo is function to one or more of the average daily air temperature, actual daily sunshine duration, measured wind speed at 2m height and relative humidity

scholarly journals Biogenic isoprene emissions driven by regional weather predictions using different initialization methods: case studies during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Vol 10 (8) ◽  
pp. 3085-3104 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Case Studies ◽  
Air Temperature ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Aircraft Observation ◽  
Near Surface ◽  
Boundary Layer Height ◽  
Small Scales

Abstract. Land and atmospheric initial conditions of the Weather Research and Forecasting (WRF) model are often interpolated from a different model output. We perform case studies during NASA's SEAC4RS and DISCOVER-AQ Houston airborne campaigns, demonstrating that using land initial conditions directly downscaled from a coarser resolution dataset led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7) surface and near-surface air temperature and planetary boundary layer height (PBLH) around the Missouri Ozarks and Houston, Texas, as well as poorly partitioned latent and sensible heat fluxes. Replacing land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF surface air temperature by ∼ 2 °C. We also show that the LIS land initialization can modify surface air temperature errors almost 10 times as effectively as applying a different atmospheric initialization method. The LIS-NUWRF-based isoprene emission calculations by the Model of Emissions of Gases and Aerosols from Nature (MEGAN, version 2.1) are at least 20 % lower than those computed using the coarser resolution data-initialized NUWRF run, and are closer to aircraft-observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified discrepancies with aircraft-observation-derived emissions on small scales. This is possibly a result of some limitations of MEGAN's parameterization and uncertainty in its inputs on small scales, as well as the representation error and the neglect of horizontal transport in deriving emissions from aircraft data. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling. We anticipate it to also be critical to accurately representing other processes included in air quality modeling and chemical data assimilation. Having more confidence in the weather inputs is also beneficial for determining and quantifying the other sources of uncertainties (e.g., parameterization, other input data) of the models that they drive.

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