scholarly journals An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern United States: observational-based analysis of surface temperature

2016 ◽  
Vol 16 (10) ◽  
pp. 6475-6494 ◽  
Author(s):  
Jianglong Zhang ◽  
Jeffrey S. Reid ◽  
Matthew Christensen ◽  
Angela Benedetti
Keyword(s):  
United States ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Cooling Efficiency ◽  
Surface Cooling ◽  
Near Surface ◽  
Smoke Aerosol ◽  
Weather Forecasts ◽  
Air Temperatures ◽  
Daily Changes

Abstract. A major continental-scale biomass burning smoke event from 28–30 June 2015, spanning central Canada through the eastern seaboard of the United States, resulted in unforecasted drops in daytime high surface temperatures on the order of 2–5  °C in the upper Midwest. This event, with strong smoke gradients and largely cloud-free conditions, provides a natural laboratory to study how aerosol radiative effects may influence numerical weather prediction (NWP) forecast outcomes. Here, we describe the nature of this smoke event and evaluate the differences in observed near-surface air temperatures between Bismarck (clear) and Grand Forks (overcast smoke), to evaluate to what degree solar radiation forcing from a smoke plume introduces daytime surface cooling, and how this affects model bias in forecasts and analyses. For this event, mid-visible (550 nm) smoke aerosol optical thickness (AOT, τ) reached values above 5. A direct surface cooling efficiency of −1.5 °C per unit AOT (at 550 nm, τ550) was found. A further analysis of European Centre for Medium-Range Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) near-surface air temperature forecasts for up to 54 h as a function of Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOT data across more than 400 surface stations, also indicated the presence of the daytime aerosol direct cooling effect, but suggested a smaller aerosol direct surface cooling efficiency with magnitude on the order of −0.25 to −1.0 °C per unit τ550. In addition, using observations from the surface stations, uncertainties in near-surface air temperatures from ECMWF, NCEP, and UKMO model runs are estimated. This study further suggests that significant daily changes in τ550 above 1, at which the smoke-aerosol-induced direct surface cooling effect could be comparable in magnitude with model uncertainties, are rare events on a global scale. Thus, incorporating a more realistic smoke aerosol field into numerical models is currently less likely to significantly improve the accuracy of near-surface air temperature forecasts. However, regions such as eastern China, eastern Russia, India, and portions of the Saharan and Taklamakan deserts, where significant daily changes in AOTs are more frequent, are likely to benefit from including an accurate aerosol analysis into numerical weather forecasts.

scholarly journals An evaluation of the impact of aerosol particles on weather forecasts from a biomass burning aerosol event over the Midwestern US: Observational-based analysis of surface temperature

2016 ◽  
Author(s):  
Jianglong Zhang ◽  
Jeffrey S. Reid ◽  
Matthew Christensen ◽  
Angela Benedetti
Keyword(s):  
Air Temperature ◽  
Biomass Burning ◽  
Surface Air Temperature ◽  
Cooling Efficiency ◽  
Surface Cooling ◽  
Near Surface ◽  
Smoke Aerosol ◽  
Weather Forecasts ◽  
Air Temperatures ◽  
Daily Changes

Abstract. A major continental scale biomass burning smoke event from June 28–30, 2015, spanning central Canada through the eastern seaboard of the United States, resulted in un-forecasted drops in daytime high surface temperatures on the order of 2–5 °C in the Upper Mid-West. This event, with strong smoke gradients and largely cloud free conditions, provides a natural laboratory to study how aerosol radiative effects may influence numerical weather prediction (NWP) forecast outcomes. Here, we describe the nature of this smoke event and evaluate the differences in observed near surface air temperatures between Bismarck (clear) and Grand Forks (overcast smoke), to evaluate to what degree solar radiation forcing from a smoke plume introduces daytime surface cooling, and how this affects model bias in forecasts and analyses. For this event, mid-visible (550 nm) smoke aerosol optical thickness (AOT, τ) reached values above five. A direct surface cooling efficiency of −1.5 °C per unit AOT (at 550 nm, τ550) was found. A further analysis of European Center for Medium range Weather Forecasting (ECMWF), National Centers for Environmental Prediction (NCEP), United Kingdom Meteorological Office (UKMO) near surface air temperature forecasts for up to 52 hours as a function of Moderate Resolution Imaging Spectroradiometer (MODIS) Dark Target AOT data across more than 400 surface stations, also indicated the presence of the daytime aerosol direct cooling effect, but suggested a smaller aerosol direct surface cooling efficiency with magnitude on the order of −0.25 °C to −1.0 °C per unit τ550. In addition, using observations from the surface stations, uncertainties in near surface air temperatures from ECMWF, NCEP and UKMO model runs are estimated. This study further suggests that significant daily changes in τ550 above 1, at which the smoke aerosol induced direct surface cooling effect could be comparable in magnitude with model uncertainties, are rare events on a global scale. Thus, incorporating a more realistic smoke aerosol field into numerical models is currently less likely to significantly improve the accuracy of near surface air temperature forecasts. However, regions such as East China, East Russian, India and portions of the Saharan and Taklamakan deserts, where significant daily changes in AOTs are more frequent, are likely to benefit from including an accurate aerosol analysis into numerical weather forecasts.

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.

Temperate-zone reforestation as a tool for local climate adaptation? 

2021 ◽  
Author(s):  
Kimberly Novick
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Climate Adaptation ◽  
Heat Waves ◽  
Surface Air Temperature ◽  
Climate Mitigation ◽  
Surface Cooling ◽  
Local Cooling ◽  
Near Surface ◽  
Local Climate

<p>In addition to dramatic reductions to anthropogenic greenhouse gas emissions, most pathways for limiting global warming to less than 2 degrees C rely on managed alterations to the land surface designed to increase land carbon uptake and storage (so-called “natural climate solutions”, or NCS). Reforestation is the NCS with the largest estimated climate mitigation potential, and at least in energy-limited temperate climates, evidence is mounting that transitions from short-stature ecosystems (croplands, grasslands) to forests substantially reduce surface temperature. In this way, reforestation, at least in some places, may also represent a useful tool for local climate adaptation. However, existing work on the topic has tended to focus on how reforestation affects mean annual and seasonal surface temperature, with comparatively less attention paid to the biophysical impacts of reforestation when local cooling would be most beneficial (i.e. at mid-day, and especially droughts and heat waves). Moreover, while surface temperature is a critical driver of ecosystem processes, arguably the near-surface air temperature is the more relevant target for climate adaptation. The duality between reforestation impacts on surface and air temperature has historically been challenging to deconvolve, and thus we do not yet understand the extent to which forest surface cooling extends to the air. In this talk, new strategies are discussed for blending flux tower data and remote sensing observations to uncover the links between reforestation, surface energy balance, and near-surface air temperature dynamics, with a particular emphasis on how plant water use strategies mediate these relationships during summer days and periods of hydrologic stress.  </p>

scholarly journals How Frequent is Precipitation over the Contiguous United States? Perspectives from Ground-Based and Spaceborne Radars

2017 ◽  
Vol 18 (6) ◽  
pp. 1657-1672 ◽  
Author(s):  
Mark Smalley ◽  
Pierre-Emmanuel Kirstetter ◽  
Tristan L’Ecuyer
Keyword(s):  
United States ◽  
Complex Terrain ◽  
Surface Air Temperature ◽  
Precipitation Frequency ◽  
Near Surface ◽  
Flat Surfaces ◽  
Air Temperatures ◽  
Temporal And Spatial ◽  
Beam Height ◽  
Precipitation Events

Abstract High temporal and spatial resolution observations of precipitation occurrence from the NEXRAD-based Multi-Radar Multi-Sensor (MRMS) system are compared to matched observations from CloudSat for 3 years over the contiguous United States (CONUS). Across the CONUS, precipitation is generally reported more frequently by CloudSat (7.8%) than by MRMS (6.3%), with dependence on factors such as the NEXRAD beam height, the near-surface air temperature, and the surface elevation. There is general agreement between ground-based and satellite-derived precipitation events over flat surfaces, especially in widespread precipitation events and when the NEXRAD beam heights are low. Within 100 km of the nearest NEXRAD site, MRMS reports a precipitation frequency of 7.54% while CloudSat reports 7.38%. However, further inspection reveals offsetting biases between the products, where CloudSat reports more snow and MRMS reports more rain. The magnitudes of these discrepancies correlate with elevation, but they are observed in both the complex terrain of the Rocky Mountains and the relatively flat midwestern areas of the CONUS. The findings advocate for caution when using MRMS frequency and accumulations in complex terrain, when temperatures are below freezing, and at ranges greater than 100 km. A multiresolution analysis shows that no more than 1.88% of CloudSat pixels over flat terrain are incorrectly identified as nonprecipitating as a result of shallow showers residing the CloudSat clutter-filled blind zone when near-surface air temperatures are above 15°C.

scholarly journals Infrared Interferometric Measurements of the Near-Surface Air Temperature over the Oceans

2005 ◽  
Vol 22 (7) ◽  
pp. 1019-1032 ◽  
Author(s):  
P. J. Minnett ◽  
K. A. Maillet ◽  
J. A. Hanafin ◽  
B. J. Osborne
Keyword(s):  
Air Temperature ◽  
Surface Air Temperature ◽  
The Arctic ◽  
Heat Island ◽  
Radiometric Measurement ◽  
Near Surface ◽  
Air Temperatures ◽  
Wide Range ◽  
Marine Air ◽  
The Tropical Pacific

Abstract The radiometric measurement of the marine air temperature using a Fourier transform infrared spectroradiometer is described. The measurements are taken by the Marine-Atmospheric Emitted Radiance Interferometer (M-AERI) that has been deployed on many research ships in a wide range of conditions. This approach is inherently more accurate than conventional techniques and can be used to determine some of the error characteristics of the standard measurements. Examples are given from several cruises ranging from the Arctic to the equatorial Pacific Oceans. It is shown that the diurnal heating signal in radiometric air temperatures in the tropical Pacific can typically reach an amplitude of ∼15% of that measured by conventional sensors. Conventional data have long been recognized as being contaminated by direct solar heating and heat island effects of the ships or buoys on which they are mounted, but here this effect is quantified by comparisons with radiometric measurements.

scholarly journals A New Model to Downscale Urban and Rural Surface and Air Temperatures Evaluated in Shanghai, China

2018 ◽  
Vol 57 (10) ◽  
pp. 2267-2283 ◽  
Author(s):  
Dongwei Liu ◽  
C. S. B. Grimmond ◽  
Jianguo Tan ◽  
Xiangyu Ao ◽  
Jie Peng ◽  
...  
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Wave Radiation ◽  
Climate Services ◽  
Near Surface ◽  
Surface Temperatures ◽  
Air Temperatures

AbstractA simple model, the Surface Temperature and Near-Surface Air Temperature (at 2 m) Model (TsT2m), is developed to downscale numerical model output (such as from ECMWF) to obtain higher-temporal- and higher-spatial-resolution surface and near-surface air temperature. It is evaluated in Shanghai, China. Surface temperature (Ts) and near-surface air temperature (Ta) submodels account for variations in land cover and their different thermal properties, resulting in spatial variations of surface and air temperature. The net all-wave radiation parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature submodel, the objective hysteresis model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near-surface air temperature submodel considers the horizontal and vertical energy changes for a column of well-mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land-cover types, values are more similar. Downscaled, higher-temporal- and higher-spatial-resolution air temperatures are compared to observations at 110 automatic weather stations across Shanghai. After downscaling with TsT2m, the average forecast accuracy of near-surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stress.

scholarly journals Improving the Stable Surface Layer in the NCEP Global Forecast System

2017 ◽  
Vol 145 (10) ◽  
pp. 3969-3987 ◽  
Author(s):  
Weizhong Zheng ◽  
Michael Ek ◽  
Kenneth Mitchell ◽  
Helin Wei ◽  
Jesse Meng
Keyword(s):  
Surface Layer ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Global Forecast System ◽  
Surface Cooling ◽  
Forecast System ◽  
Near Surface ◽  
Stable Surface ◽  
Stable Surface Layer ◽  
Ncep Global Forecast System

This study examines the performance of the NCEP Global Forecast System (GFS) surface layer parameterization scheme for strongly stable conditions over land in which turbulence is weak or even disappears because of high near-surface atmospheric stability. Cases of both deep snowpack and snow-free conditions are investigated. The results show that decoupling and excessive near-surface cooling may appear in the late afternoon and nighttime, manifesting as a severe cold bias of the 2-m surface air temperature that persists for several hours or more. Concurrently, because of negligible downward heat transport from the atmosphere to the land, a warm temperature bias develops at the first model level. The authors test changes to the stable surface layer scheme that include introduction of a stability parameter constraint that prevents the land–atmosphere system from fully decoupling and modification to the roughness-length formulation. GFS sensitivity runs with these two changes demonstrate the ability of the proposed surface layer changes to reduce the excessive near-surface cooling in forecasts of 2-m surface air temperature. The proposed changes prevent both the collapse of turbulence in the stable surface layer over land and the possibility of numerical instability resulting from thermal decoupling between the atmosphere and the surface. The authors also execute and evaluate daily GFS 7-day test forecasts with the proposed changes spanning a one-month period in winter. The assessment reveals that the systematic deficiencies and substantial errors in GFS near-surface 2-m air temperature forecasts are considerably reduced, along with a notable reduction of temperature errors throughout the lower atmosphere and improvement of forecast skill scores for light and medium precipitation amounts.

scholarly journals Estimating radar precipitation in cold climates: the role of air temperature within a non-parametric framework

2018 ◽  
Vol 22 (12) ◽  
pp. 6533-6546 ◽  
Author(s):  
Kuganesan Sivasubramaniam ◽  
Ashish Sharma ◽  
Knut Alfredsen
Keyword(s):  
Predictive Model ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Precipitation Rate ◽  
Cold Climates ◽  
Near Surface ◽  
Air Temperatures ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Non Parametric

Abstract. The use of ground-based precipitation measurements in radar precipitation estimation is well known in radar hydrology. However, the approach of using gauged precipitation and near-surface air temperature observations to improve radar precipitation estimates in cold climates is much less common. In cold climates, precipitation is in the form of snow, rain or a mixture of the two phases. Air temperature is intrinsic to the phase of the precipitation and could therefore be a possible covariate in the models used to ascertain radar precipitation estimates. In the present study, we investigate the use of air temperature within a non-parametric predictive framework to improve radar precipitation estimation for cold climates. A non-parametric predictive model is constructed with radar precipitation rate and air temperature as predictor variables and gauge precipitation as an observed response using a k nearest neighbour (k-nn) regression estimator. The relative importance of the two predictors is ascertained using an information theory-based weighting. Four years (2011–2015) of hourly radar precipitation rates from the Norwegian national radar network over the Oslo region, hourly gauged precipitation from 68 gauges and gridded observational air temperatures were used to formulate the predictive model, hence making our investigation possible. Gauged precipitation data were corrected for wind-induced under-catch before using them as true observed response. The predictive model with air temperature as an added covariate reduces root-mean-square error (RMSE) by up to 15 % compared to the model that uses radar precipitation rate as the sole predictor. More than 80 % of gauge locations in the study area showed improvement with the new method. Further, the associated impact of air temperature became insignificant at more than 85 % of gauge locations when the near-surface air temperature was warmer than 10 ∘C, which indicates that the partial dependence of precipitation on air temperature is most useful for colder temperatures.

scholarly journals Remotely Sensed Land Skin Temperature as a Spatial Predictor of Air Temperature across the Conterminous United States

2016 ◽  
Vol 55 (7) ◽  
pp. 1441-1457 ◽  
Author(s):  
Jared W. Oyler ◽  
Solomon Z. Dobrowski ◽  
Zachary A. Holden ◽  
Steven W. Running
Keyword(s):  
United States ◽  
Skin Temperature ◽  
Air Temperature ◽  
Arid Regions ◽  
Linear Models ◽  
Surface Air Temperature ◽  
Remotely Sensed ◽  
Near Surface ◽  
The Relationship ◽  
Maximum Air Temperature

AbstractRemotely sensed land skin temperature (LST) is increasingly being used to improve gridded interpolations of near-surface air temperature. The appeal of LST as a spatial predictor of air temperature rests in the fact that it is an observation available at spatial resolutions fine enough to capture topoclimatic and biophysical variations. However, it remains unclear if LST improves air temperature interpolations over what can already be obtained with simpler terrain-based predictor variables. Here, the relationship between LST and air temperature is evaluated across the conterminous United States (CONUS). It is found that there are significant differences in the ability of daytime and nighttime observations of LST to improve air temperature interpolations. Daytime LST mainly indicates finescale biophysical variation and is generally a poorer predictor of maximum air temperature than simple linear models based on elevation, longitude, and latitude. Moderate improvements to maximum air temperature interpolations are thus limited to specific mountainous areas in winter, to coastal areas, and to semiarid and arid regions where daytime LST likely captures variations in evaporative cooling and aridity. In contrast, nighttime LST represents important topoclimatic variation throughout the mountainous western CONUS and significantly improves nighttime minimum air temperature interpolations. In regions of more homogenous terrain, nighttime LST also captures biophysical patterns related to land cover. Both daytime and nighttime LST display large spatial and seasonal variability in their ability to improve air temperature interpolations beyond simpler approaches.

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