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.

scholarly journals Linkages between land initialization of the NASA-Unified WRF v7 and biogenic isoprene emission estimates during the SEAC<sup>4</sup>RS and DISCOVER-AQ airborne campaigns

2017 ◽  
Author(s):  
Min Huang ◽  
Gregory R. Carmichael ◽  
James H. Crawford ◽  
Armin Wisthaler ◽  
Xiwu Zhan ◽  
...  
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Initial Conditions ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Heat Fluxes ◽  
Temperature Fields ◽  
Surface Model ◽  
Near Surface ◽  
Isoprene Emission

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 initializing the Noah land surface model directly using a coarser resolution dataset North American Regional Reanalysis (NARR) led to significant positive biases in the coupled NASA-Unified WRF (NUWRF, version 7)'s (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 the land initial conditions with the output from a long-term offline Land Information System (LIS) simulation can effectively reduce the positive biases in NUWRF's surface air temperature fields by ~ 2 °C. We also show that the LIS land initialization can modify the surface air temperature errors almost ten 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 NARR-initialized NUWRF run, and are closer to the aircraft observation-derived emissions. Higher resolution MEGAN calculations are prone to amplified errors on small scales, possibly resulted from some limitations of MEGAN's parameterization and its inputs' uncertainty. This study emphasizes the importance of proper land initialization to the coupled atmospheric weather modeling and the follow-on emission modeling, which we anticipate to be also 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.

Retrieval of Hourly Records of Surface Hydrometeorological Variables Using Satellite Remote Sensing Data

2015 ◽  
Vol 16 (1) ◽  
pp. 147-157 ◽  
Author(s):  
Sanaz Moghim ◽  
Andrew Jay Bowen ◽  
Sepideh Sarachi ◽  
Jingfeng Wang
Keyword(s):  
Remote Sensing ◽  
Air Temperature ◽  
Sensible Heat Flux ◽  
Surface Air Temperature ◽  
Remote Sensing Data ◽  
Heat Fluxes ◽  
Production Model ◽  
Integral Model ◽  
Near Surface ◽  
Surface Heat Fluxes

Abstract A new algorithm is formulated for retrieving hourly time series of surface hydrometeorological variables including net radiation, sensible heat flux, and near-surface air temperature aided by hourly visible images from the Geostationary Operational Environmental Satellite (GOES) and in situ observations of mean daily air temperature. The algorithm is based on two unconventional, recently developed methods: the maximum entropy production model of surface heat fluxes and the half-order derivative–integral model that has been tested previously. The close agreement between the retrieved hourly variables using remotely sensed input and the corresponding field observations indicates that this algorithm is an effective tool in remote sensing of the earth system.

scholarly journals Suitability of a constant air temperature lapse rate over an Alpine glacier: testing the Greuell and Böhm model as an alternative

2013 ◽  
Vol 54 (63) ◽  
pp. 120-130 ◽  
Author(s):  
Lene Petersen ◽  
Francesca Pellicciotti ◽  
Inge Juszak ◽  
Marco Carenzo ◽  
Ben Brock
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
Ambient Air ◽  
Lapse Rate ◽  
Spatial And Temporal Variability ◽  
Near Surface ◽  
Alpine Glacier ◽  
Temperature Lapse Rate ◽  
Boundary Layer Height ◽  
Air Temperatures

AbstractNear-surface air temperature, typically measured at a height of 2 m, is the most important control on the energy exchange and the melt rate at a snow or ice surface. It is distributed in a simplistic manner in most glacier melt models by using constant linear lapse rates, which poorly represent the actual spatial and temporal variability of air temperature. In this paper, we test a simple thermodynamic model proposed by Greuell and Böhm in 1998 as an alternative, using a new dataset of air temperature measurements from along the flowline of Haut Glacier d’Arolla, Switzerland. The unmodified model performs little better than assuming a constant linear lapse rate. When modified to allow the ratio of the boundary layer height to the bulk heat transfer coefficient to vary along the flowline, the model matches measured air temperatures better, and a further reduction of the root-mean-square error is obtained, although there is still considerable scope for improvement. The modified model is shown to perform best under conditions favourable to the development of katabatic winds – few clouds, positive ambient air temperature, limited influence of synoptic or valley winds and a long fetch – but its performance is poor under cloudy conditions.

scholarly journals Summer- and Wintertime Variations of the Surface and Near-Surface Urban Heat Island in a Semiarid Environment

2019 ◽  
Vol 34 (6) ◽  
pp. 1849-1865
Author(s):  
Francisco Salamanca Palou ◽  
Alex Mahalov
Keyword(s):  
Air Temperature ◽  
Land Surface ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Near Surface ◽  
Urban Rural ◽  
Urban Cool Island ◽  
Surface Skin Temperature ◽  
Urban Heat ◽  
Surface Urban Heat Island

Abstract This paper examines summer- and wintertime variations of the surface and near-surface urban heat island (UHI) for the Phoenix metropolitan area using the Moderate Resolution Imaging Spectroradiometer (MODIS), near-surface meteorological observations, and the Weather Research and Forecasting (WRF) Model during a 31-day summer- and a 31-day wintertime period. The surface UHI (defined based on the urban–rural land surface temperature difference) is found to be higher at night and during the warm season. On the other hand, the morning surface UHI is low and frequently exhibits an urban cool island that increases during the summertime period. Similarly, the near-surface UHI (defined based on the urban–rural 2-m air temperature difference) is higher at night and during summertime. On the other hand, the daytime near-surface UHI is low but rarely exhibits an urban cool island. To evaluate the WRF Model’s ability to reproduce the diurnal cycle of near-surface meteorology and surface skin temperature, two WRF Model experiments (one using the Bougeault and Lacarrere turbulent scheme and one with the Mellor–Yamada–Janjić turbulent parameterization) at high spatial resolution (1-km horizontal grid spacing) are conducted for each 31-day period. Modeled results show that the WRF Model (coupled to the Noah-MP land surface model) tends to underestimate to some extent surface skin temperature during daytime and overestimate nighttime values during the wintertime period. In the same way, the WRF Model tends to accurately reproduce the diurnal cycle of near-surface air temperature, including maximum and minimum temperatures, and wind speed during summertime, but notably overestimates nighttime near-surface air temperature during wintertime. This nighttime overestimation is especially remarkable with the Bougeault and Lacarrere turbulent scheme for both urban and rural areas.

Quantifying Spatiotemporal Variations of Soil Moisture Control on Surface Energy Balance and Near-Surface Air Temperature

2017 ◽  
Vol 30 (18) ◽  
pp. 7105-7124 ◽  
Author(s):  
Clemens Schwingshackl ◽  
Martin Hirschi ◽  
Sonia I. Seneviratne
Keyword(s):  
Soil Moisture ◽  
Air Temperature ◽  
Full Range ◽  
Surface Air Temperature ◽  
Temporal Variations ◽  
Heat Fluxes ◽  
Moisture Regime ◽  
Transitional Regime ◽  
Evaporative Fraction ◽  
Near Surface

Abstract Soil moisture plays a crucial role for the energy partitioning at Earth’s surface. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect both near-surface air temperature and precipitation. In this study, a simple framework for the dependence of evaporative fraction (the ratio of latent heat flux over net radiation) on soil moisture is used to analyze spatial and temporal variations of land–atmosphere coupling and its effect on near-surface air temperature. Using three different data sources (two reanalysis datasets and one combination of different datasets), three key parameters for the relation between soil moisture and evaporative fraction are estimated: 1) the frequency of occurrence of different soil moisture regimes, 2) the sensitivity of evaporative fraction to soil moisture in the transitional soil moisture regime, and 3) the critical soil moisture value that separates soil moisture- and energy-limited evapotranspiration regimes. The results show that about 30%–60% (depending on the dataset) of the global land area is in the transitional regime during at least half of the year. Based on the identification of transitional regimes, the effect of changes in soil moisture on near-surface air temperature is analyzed. Typical soil moisture variations (standard deviation) can impact air temperature by up to 1.1–1.3 K, while changing soil moisture over its full range in the transitional regime can alter air temperature by up to 6–7 K. The results emphasize the role of soil moisture for atmosphere and climate and constitute a useful benchmark for the evaluation of the respective relationships in Earth system models.

scholarly journals Atmospheric Processes and Climatological Characteristics of the 79N Glacier (Northeast Greenland)

2019 ◽  
Vol 147 (4) ◽  
pp. 1375-1394 ◽  
Author(s):  
Jenny V. Turton ◽  
Thomas Mölg ◽  
Dirk Van As
Keyword(s):  
Air Temperature ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Greenland Ice Sheet ◽  
Cloud Formation ◽  
Atmospheric Conditions ◽  
Subsequent Increase ◽  
Near Surface ◽  
Surface Mass ◽  
Atmospheric Processes

Abstract The Nioghalvfjerdsfjorden glacier (the 79 fjord, henceforth referred to as 79N) has been thinning and accelerating since the early 2000s, as a result of calving episodes at the front of the glacier. As 8% of the Greenland Ice Sheet area drains into 79N, changes in the stability of 79N could propagate into the interior of Greenland. Despite this concern, relatively little is known about the atmospheric conditions over 79N. We present the surface atmospheric processes and climatology of the 79N region from analyses of data from four automatic weather stations (AWS) and reanalysis data from ERA-Interim. Over the floating section of the glacier, the annual average air temperature is −16.7°C, decreasing to −28.5°C during winter. Winds over the glacier are predominantly westerly and are of katabatic origin. Over the last 39 years the near-surface air temperature has increased at a rate of +0.08°C yr−1. In addition, we find that large, rapid (48 h) temperature increases (&gt;10°C) occur during the five-month dark period (November–March). Eight (±4) warm-air events occur annually from 1979 to 2017. We use the Weather Research and Forecasting (WRF) Model to simulate a particular warm-air event with above-freezing air temperatures between 30 November and 2 December 2014. The warm event was caused by warm-air advection from the southeast and a subsequent increase in the longwave radiation toward the surface due to low-level cloud formation. The frequent nature of the temperature jumps and the magnitude of the temperature increases are likely to have an impact on the surface mass balance of the glacier by bringing the skin temperatures to the melting point.

scholarly journals The Effects of Anthropogenic Heat Release on Urban Meteorology and Implication for Haze Pollution in the Beijing-Tianjin-Hebei Region

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ruiting Liu ◽  
Zhiwei Han
Keyword(s):  
Heat Release ◽  
Air Temperature ◽  
Wrf Model ◽  
Surface Air Temperature ◽  
Urban Canopy ◽  
Anthropogenic Heat ◽  
Hygroscopic Growth ◽  
Near Surface ◽  
Atmospheric Diffusion ◽  
Haze Pollution

In this study, the effect of anthropogenic heat release (AHR) on meteorological variables and atmospheric diffusion capability and implication for haze pollution in the Beijing-Tianjin-Hebei region in January 2013 were investigated by using Weather Research and Forecasting (WRF) model with an urban canopy model (UCM) and an AHR scheme. The comparison with observation demonstrated the WRF/UCM model taking AHR into account apparently improved meteorological prediction, especially for surface air temperature at 2 m (T2). The model also exhibited a better performance for planetary boundary layer (PBL) height. This study revealed that AHR from cities exerted a significant impact on meteorology by generally increasing surface air temperature and wind speed, decreasing relative humidity, and elevating PBL height and near surface turbulent kinetic energy (TKE), which could consequently reduce surface pollutant concentration and mitigate haze pollution by enhancing atmospheric instability and turbulent mixing and reducing aerosol hygroscopic growth.

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 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

&lt;p&gt;Reliable projections of extremes in near-surface air temperature (SAT) by climate models become more and more important&amp;#160;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

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