Calibrating and Evaluating Reanalysis Surface Temperature Error by Topographic Correction

2008 ◽  
Vol 21 (6) ◽  
pp. 1440-1446 ◽  
Author(s):  
Tianbao Zhao ◽  
Weidong Guo ◽  
Congbin Fu
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Original Data ◽  
Reanalysis Data ◽  
Temperature Data ◽  
Temperature Error ◽  
Topographic Correction ◽  
Temperature Errors

Abstract Based on the observed daily surface air temperature data from 597 stations over continental China and two sets of reanalysis data [NCEP–NCAR and 40-yr ECMWF Re-Analysis (ERA-40)] during 1979–2001, the altitude effects in calibrating and evaluating reanalyzed surface temperature errors are studied. The results indicate that the accuracy of interpolated surface temperature from the reanalyzed gridpoint value or the station observations depends much on the altitudes of original data. Bias of interpolated temperature is usually in proportion to the increase of local elevation and topographical complexity. Notable improvements of interpolated surface temperature have been achieved through “topographic correction,” especially for ERA-40, which highlights the necessity of removal of “elevation-induced bias” when using and evaluating reanalyzed surface temperature.

scholarly journals Subseasonal relationship between Arctic and Eurasian surface air temperature

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hye-Jin Kim ◽  
Seok-Woo Son ◽  
Woosok Moon ◽  
Jong-Seong Kug ◽  
Jaeyoung Hwang
Keyword(s):  
Air Temperature ◽  
Diabatic Heating ◽  
Surface Air Temperature ◽  
Significant Negative Correlation ◽  
Cold Season ◽  
Reanalysis Data ◽  
Necessary Condition ◽  
Sea Level Pressure ◽  
Blocking High ◽  
Pressure Anomaly

AbstractThe subseasonal relationship between Arctic and Eurasian surface air temperature (SAT) is re-examined using reanalysis data. Consistent with previous studies, a significant negative correlation is observed in cold season from November to February, but with a local minimum in late December. This relationship is dominated not only by the warm Arctic-cold Eurasia (WACE) pattern, which becomes more frequent during the last two decades, but also by the cold Arctic-warm Eurasia (CAWE) pattern. The budget analyses reveal that both WACE and CAWE patterns are primarily driven by the temperature advection associated with sea level pressure anomaly over the Ural region, partly cancelled by the diabatic heating. It is further found that, although the anticyclonic anomaly of WACE pattern mostly represents the Ural blocking, about 20% of WACE cases are associated with non-blocking high pressure systems. This result indicates that the Ural blocking is not a necessary condition for the WACE pattern, highlighting the importance of transient weather systems in the subseasonal Arctic-Eurasian SAT co-variability.

scholarly journals An updated evaluation of the global mean land surface air temperature and surface temperature trends based on CLSAT and CMST

2021 ◽  
Vol 56 (1-2) ◽  
pp. 635-650 ◽  
Author(s):  
Qingxiang Li ◽  
Wenbin Sun ◽  
Xiang Yun ◽  
Boyin Huang ◽  
Wenjie Dong ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Air Temperature ◽  
Land Surface ◽  
Surface Air Temperature ◽  
Temperature Trends ◽  
Global Mean

scholarly journals <i>Brief communication</i> "A statistical validation for the cycles found in air temperature data using a Morlet wavelet-based method"

2010 ◽  
Vol 17 (3) ◽  
pp. 269-272 ◽  
Author(s):  
S. Nicolay ◽  
G. Mabille ◽  
X. Fettweis ◽  
M. Erpicum
Keyword(s):  
Air Temperature ◽  
Climate Model ◽  
Surface Air Temperature ◽  
Weather Forecast ◽  
Temperature Data ◽  
Temperature Time Series ◽  
Climatic Data ◽  
Statistical Validation ◽  
Confidence Levels ◽  
Temperature Time

Abstract. Recently, new cycles, associated with periods of 30 and 43 months, respectively, have been observed by the authors in surface air temperature time series, using a wavelet-based methodology. Although many evidences attest the validity of this method applied to climatic data, no systematic study of its efficiency has been carried out. Here, we estimate confidence levels for this approach and show that the observed cycles are significant. Taking these cycles into consideration should prove helpful in increasing the accuracy of the climate model projections of climate change and weather forecast.

scholarly journals Near–surface air temperature and snow skin temperature comparison from CREST-SAFE station data with MODIS land surface temperature data

2015 ◽  
Vol 12 (8) ◽  
pp. 7665-7687 ◽  
Author(s):  
C. L. Pérez Díaz ◽  
T. Lakhankar ◽  
P. Romanov ◽  
J. Muñoz ◽  
R. Khanbilvardi ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Skin Temperature ◽  
Air Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Critical Role ◽  
Surface Air Temperature ◽  
Balance Model ◽  
Near Surface

Abstract. Land Surface Temperature (LST) is a key variable (commonly studied to understand the hydrological cycle) that helps drive the energy balance and water exchange between the Earth's surface and its atmosphere. One observable constituent of much importance in the land surface water balance model is snow. Snow cover plays a critical role in the regional to global scale hydrological cycle because rain-on-snow with warm air temperatures accelerates rapid snow-melt, which is responsible for the majority of the spring floods. Accurate information on near-surface air temperature (T-air) and snow skin temperature (T-skin) helps us comprehend the energy and water balances in the Earth's hydrological cycle. T-skin is critical in estimating latent and sensible heat fluxes over snow covered areas because incoming and outgoing radiation fluxes from the snow mass and the air temperature above make it different from the average snowpack temperature. This study investigates the correlation between MODerate resolution Imaging Spectroradiometer (MODIS) LST data and observed T-air and T-skin data from NOAA-CREST-Snow Analysis and Field Experiment (CREST-SAFE) for the winters of 2013 and 2014. LST satellite validation is imperative because high-latitude regions are significantly affected by climate warming and there is a need to aid existing meteorological station networks with the spatially continuous measurements provided by satellites. Results indicate that near-surface air temperature correlates better than snow skin temperature with MODIS LST data. Additional findings show that there is a negative trend demonstrating that the air minus snow skin temperature difference is inversely proportional to cloud cover. To a lesser extent, it will be examined whether the surface properties at the site are representative for the LST properties within the instrument field of view.

Surface air–soil temperature relationship and shallow soil thermal regime: a case study using a soil temperature observational dataset for Spain.

2021 ◽  
Author(s):  
Camilo Melo Aguilar ◽  
Fidel González Rouco ◽  
Norman Steinert ◽  
Elena García Bustamante ◽  
Felix García Pereira ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Soil Temperature ◽  
Air Temperature ◽  
Thermal Regime ◽  
Ground Surface ◽  
Surface Air Temperature ◽  
Environmental Variable ◽  
Chemical Processes ◽  
Strong Connection ◽  
Shallow Subsurface

&lt;p&gt;The land-atmosphere interactions via the energy and water exchanges at the ground surface generally translate into a strong connection between the surface air temperature (SAT) and the ground surface temperature (GST). In turn, the surface temperature affects the amount of heat flowing into the soil, thus controlling the subsurface temperature profile. As soil temperature (ST) is a key environmental variable that controls various physical, biological and chemical processes, understanding the relationship between SAT and GST and STs is important.&lt;/p&gt;&lt;p&gt;In situ ST measurements represent the most adequate source&amp;#160;of information to evaluate the distribution of temperature in soils and to address its influence on soil biological and chemical processes as well as on climate feedbacks. However, ST observations are scarce both in space and time. Therefore, the development of ST observational datasets is of great interest to promote analyses regarding the soil thermodynamics and the response to atmospheric warming.&lt;/p&gt;&lt;p&gt;We have developed a quality-controlled dataset of Soil Temperature Observations for Spain (SoTOS). The ST data are obtained from the Spanish meteorological agency (AEMET), including ST at different layers down to a depth of 1 m (i.e., 0.05, 0.1, 0.2, 0.5 and 1 m depth) for 39 observatories for the 1985&amp;#8211;2018 period. Likewise, 2m air temperature has also been included for the same 39 sites.&lt;/p&gt;&lt;p&gt;SoTOS is employed to evaluate the shallow subsurface thermal regime and the SAT&amp;#8211;GST relationship on interannual to multidecadal timescales. The results show that thermal conduction is the main heat transfer mechanism that controls the distribution of soil temperatures in the shallow subsurface. Regarding the SAT-GST relationship, there is a strong connection between SAT and GST. However, the SAT&amp;#8211;GST coupling may be disrupted on seasonal to multidecadal timescales due to variations in the surface energy balance in response to decreasing soil moisture conditions over the last decade at some SoTOS sites. This results in larger GST warming relative to SAT. Such a response may have implications for climate studies that assume a strong connection between SAT and GST such as air temperature estimations from remote sensing products or even for palaeoclimatic analyses.&lt;/p&gt;

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